diff --git a/spaces/1gistliPinn/ChatGPT4/Crea Il Tuo Personaggio Cartoon Fix.md b/spaces/1gistliPinn/ChatGPT4/Crea Il Tuo Personaggio Cartoon Fix.md
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-## crea il tuo personaggio cartoon
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-# Come creare il tuo personaggio cartoon online gratis
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diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Enjoy Lords of the Realm III Without Verification Download Here.md b/spaces/1gistliPinn/ChatGPT4/Examples/Enjoy Lords of the Realm III Without Verification Download Here.md
deleted file mode 100644
index 23c309b5abbcfb1df820093ee6b7af44f501b069..0000000000000000000000000000000000000000
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+++ /dev/null
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8.2 We do not pre-screen, approve, endorse, or own Your UGC (as well as UGC of other users of the Game), which You have uploaded or made available to other users via the Game or the Services. You create, download, and use the User Generated Content at Your own risk. Still by uploading or making available Your UGC via the Game or the Services, You grant us a non-exclusive, transferable, sublicensable, worldwide, irrevocable license to store, publish, print, distribute, reproduce, copy, fix, perform, adapt, modify, transfer, and use for commercial purposes (including, but not limited to, the use in for advertisement purposes) Your UGC without any notice or further compensation to You.
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diff --git a/spaces/1phancelerku/anime-remove-background/Dark Riddle Hack How I Beat the Game with Unlimited Resources.md b/spaces/1phancelerku/anime-remove-background/Dark Riddle Hack How I Beat the Game with Unlimited Resources.md
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Dark Riddle Hack: How to Unlock All Skins and Quests
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If you are a fan of stealth games, you might have heard of Dark Riddle, a popular mobile game that lets you explore your neighbor's house and discover his secrets. But did you know that you can use a hack tool to unlock all the skins and quests in the game? In this article, we will show you how to do that and more.
Dark Riddle is a 3D adventure game developed by Nika Entertainment. It is available for both Android and iOS devices. In this game, you play as a curious character who wants to find out what your neighbor is hiding in his basement. You have to sneak into his house, avoid his traps and cameras, and solve puzzles to progress. You can also interact with various objects and characters in the game, such as a cat, a dog, a crow, a pizza delivery guy, and more.
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The game has many features that make it fun and challenging, such as:
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Different modes of difficulty: easy, normal, hard, and nightmare.
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Dark Riddle is a free-to-play game, but it also has some in-app purchases that can enhance your gaming experience. For example, you can buy coins, gems, keys, hints, and premium skins using real money. However, not everyone can afford or want to spend money on these items. That's where Dark Riddle hack comes in handy.
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By using Dark Riddle hack, you can get access to a hidden menu that gives you unlimited resources and options. You can use these to:
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Unlock all skins and quests in the game without spending any money or completing any tasks.
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Get unlimited coins, gems, keys, hints, and energy to use in the game.
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Change your character's speed, size, gravity, invisibility, and other parameters.
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Have fun with different cheats, such as flying mode, ghost mode, god mode, etc.
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However, using Dark Riddle hack also comes with some risks that you should be aware of. These include:
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Therefore, you should use Dark Riddle the hack tool to change your size, but don't set it too big or small or you will have trouble with doors or objects.
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Use your gravity wisely. Gravity can help you jump higher or lower, but it also affects your landing and balance. You can use the hack tool to change your gravity, but don't set it too high or low or you will fall hard or float away.
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Use your invisibility carefully. Invisibility can help you avoid detection or surprise your enemy, but it also affects your interaction and vision. You can use the hack tool to make yourself invisible, but don't set it too long or short or you will miss some actions or alerts.
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Use your cheats sparingly. Cheats can help you have fun with different modes, such as flying mode, ghost mode, god mode, etc. You can use the hack tool to activate them, but don't abuse them or you will lose the fun and challenge of the game.
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Use your settings moderately. Settings can help you modify the game's environment, such as the time of day, the weather, the sound effects, and more. You can use the hack tool to change them, but don't alter them too much or you will ruin the atmosphere and realism of the game.
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Dark Riddle is a great game that offers a lot of fun and challenge for stealth game lovers. However, if you want to unlock all the skins and quests in the game without spending any money or completing any tasks, you can use Dark Riddle hack. This hack tool allows you to access a hidden menu that gives you unlimited resources and options to customize your game experience. However, you should also be aware of the risks of using the hack tool and use it with caution and moderation. We hope this article has helped you learn how to use Dark Riddle hack and enjoy the game more.
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A: Dark Riddle hack is free to use and does not require any payment or registration. However, you should also respect the developers of the game and support them by buying some in-app purchases if you can afford them.
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A: Dark Riddle hack is easy to use and does not require any technical skills or knowledge. You just need to follow some simple steps to access the hack menu and choose the options you want.
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A: Dark Riddle hack is compatible with all devices that can run the game, such as Android and iOS devices. However, you should also make sure that your device has enough storage space and battery life to run the game and the hack tool smoothly.
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A: Dark Riddle hack is updated regularly to keep up with the latest version of the game and fix any bugs or errors. However, you should also check for updates frequently and download them as soon as they are available.
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diff --git a/spaces/1phancelerku/anime-remove-background/Download 8 Ball Pool for Java and Play with Friends Online.md b/spaces/1phancelerku/anime-remove-background/Download 8 Ball Pool for Java and Play with Friends Online.md
deleted file mode 100644
index ddb4e7b9e397c28f4066d0db5143f1c540794c97..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Download 8 Ball Pool for Java and Play with Friends Online.md
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Download 8 Ball Pool for Java: A Guide for Pool Lovers
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If you are a fan of pool games, you might have heard of 8 Ball Pool, one of the most popular and addictive online pool games in the world. But did you know that you can also play it on your Java platform? In this article, we will show you how to download and play 8 Ball Pool on Java, as well as some tips and tricks to improve your game. Let's get started!
8 Ball Pool is an online pool game where you can compete with other players from around the world in various game modes, such as PvP, Tournaments, 9 Ball, and more. You can also customize your cue and table, buy new items in the Pool Shop, and join clubs to chat with other players. The game is developed by Miniclip, a leading online gaming company, and has over 500 million downloads on Google Play. It is also available on other platforms, such as iOS, Windows, and web browsers.
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What is Java Platform?
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Java platform is a suite of programs that facilitate developing and running programs written in the Java programming language. It includes an execution engine (called a virtual machine), a compiler, and a set of libraries. Java platform is independent of any particular operating system, which makes Java programs run identically on all of them. Java is a programming language and computing platform first released by Sun Microsystems in 1995. The Java Virtual Machine (JVM) is platform-dependent, meaning that different operating systems require different JVMs.
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How to Download 8 Ball Pool for Java?
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To download and play 8 Ball Pool on Java, you need to follow these steps:
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Step 1: Check your system requirements and compatibility
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Before you download the game, make sure that your device meets the minimum system requirements for running Java programs. You can check them here. Also, make sure that your device supports the Jar format, which is the file format for Java applications. You can check this by looking at the file extension of your downloaded files. If they end with .jar. , then they are Jar files and you can run them on your Java platform. If they end with .jad, then they are Jad files and you need to convert them to Jar files first. You can use online tools like this one to do that.
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Step 2: Download and install Java Runtime Environment (JRE)
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Java Runtime Environment (JRE) is a software package that provides the libraries and components needed to run Java applications. You can download the latest version of JRE from the official website. Choose the version that matches your operating system and device architecture. After downloading, follow the instructions to install JRE on your device.
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Step 3: Download and install 8 Ball Pool from a trusted source
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There are many websites that offer 8 Ball Pool for Java, but not all of them are safe and reliable. Some of them may contain malware or viruses that can harm your device or steal your personal information. To avoid this, you should only download 8 Ball Pool from a trusted source, such as the official Miniclip website. You can also use other reputable sources, such as Mobile9 or Phoneky. After downloading, locate the Jar file on your device and open it to install 8 Ball Pool on your Java platform.
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How to Play 8 Ball Pool on Java?
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Playing 8 Ball Pool on Java is similar to playing it on other platforms, but there are some differences in the controls and interface. Here are the steps to play 8 Ball Pool on Java:
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Step 1: Launch the game and sign in with your account
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After installing 8 Ball Pool on your Java platform, launch the game by clicking on its icon. You will see the main menu with several options, such as Play Online, Play Offline, Pool Shop, Settings, and more. To play online, you need to sign in with your Miniclip account or create one if you don't have one. You can also sign in with your Facebook account if you want to sync your progress and access your friends list. To play offline, you don't need to sign in, but you will have limited features and modes.
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Step 2: Choose a game mode and a table
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After signing in, you can choose a game mode from the following options: PvP, where you can play against another player in real time; Tournaments, where you can join a bracket of players and compete for prizes; 9 Ball, where you can play a different variation of pool with only nine balls; and Practice, where you can practice your shots without any pressure. You can also choose a table from different themes and styles, such as London, Sydney, Moscow, and more. Each table has a different entry fee and reward, so choose wisely according to your skill level and budget.
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Step 3: Aim and shoot your cue ball
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Once you enter a game, you will see the pool table with the balls arranged in a triangle. The game follows the standard rules of 8 ball pool, which means that you have to pot all the balls of your assigned group (solid or striped) before potting the black 8 ball. To aim your cue ball, use the arrow keys or the number keys on your keypad. To adjust the power of your shot, use the * key or the # key on your keypad. To shoot, press the OK button or the 5 key on your keypad. You can also use the spin feature by pressing the left soft key or the right soft key on your keypad. This will allow you to control the direction and speed of your cue ball after hitting another ball.
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Tips and Tricks for Playing 8 Ball Pool on Java
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To improve your game and have more fun playing 8 Ball Pool on Java, here are some tips and tricks that you should know:
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Tip 1: Use the spin feature to control the cue ball
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The spin feature is one of the most useful tools in 8 Ball Pool, as it can help you avoid scratches, get better positions, and make trick shots. To use it, press the left soft key or the right soft key on your keypad when aiming your cue ball. You will see a circle with four arrows around it, indicating the direction of the spin. You can choose from four types of spin: top spin, back spin, left spin, and right spin. Each type of spin has a different effect on the cue ball's movement and angle. For example, top spin will make the cue ball move forward after hitting another ball, while back spin will make it move backward. Left spin and right spin will make the cue ball curve to the left or right, respectively. You can use these effects to avoid obstacles, get closer to your target ball, or make difficult shots.
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Tip 2: Practice your shots in offline mode
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If you want to improve your skills and confidence in 8 Ball Pool, you should practice your shots in offline mode. Offline mode allows you to play against the computer or yourself without any internet connection or entry fee. You can choose from different difficulty levels and table themes, and you can also adjust the rules and settings of the game. Offline mode is a great way to learn the basics of the game, test your strategies, and have fun without any pressure or risk.
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Tip 3: Challenge your friends and other players online
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One of the best features of 8 Ball Pool is that you can challenge your friends and other players online. You can invite your friends from Facebook or Miniclip to play with you, or you can join a random match with someone from anywhere in the world. You can also chat with your opponent during the game, send them emojis, and add them as friends. Playing online is not only fun and exciting, but also rewarding and challenging. You can earn coins, items, trophies, and ranking points by winning matches, and you can also join clubs and tournaments to compete with other players and teams.
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Conclusion
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8 Ball Pool is a fantastic online pool game that you can play on your Java platform. It has amazing graphics, realistic physics, and addictive gameplay. You can download it from a trusted source, install it on your device, and enjoy playing it anytime and anywhere. You can also use some tips and tricks to improve your game and have more fun. Whether you are a beginner or a pro, 8 Ball Pool has something for everyone. So what are you waiting for? Download 8 Ball Pool for Java today and join the millions of pool lovers around the world!
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FAQs
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Q1: Is 8 Ball Pool free to play on Java?
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A1: Yes, 8 Ball Pool is free to play on Java. However, some features and items may require in-app purchases or real money.
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Q2: How can I get more coins and items in 8 Ball Pool?
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A2: You can get more coins and items in 8 Ball Pool by winning matches, completing missions, spinning the wheel, watching ads, or buying them with real money.
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Q3: How can I improve my skills and ranking in 8 Ball Pool?
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A3: You can improve your skills and ranking in 8 Ball Pool by practicing your shots in offline mode, learning from other players online, using the spin feature wisely, and joining clubs and tournaments.
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Q4: What are the differences between 8 Ball Pool on Java and other platforms?
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A4: The main differences between 8 Ball Pool on Java and other platforms are the controls and interface. On Java platform, you use the keypad to aim and shoot your cue ball, while on other platforms, you use the touch screen or the mouse. The interface on Java platform is also simpler and less cluttered than on other platforms.
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Q5: What are the best sources to download 8 Ball Pool for Java?
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A5: The best sources to download 8 Ball Pool for Java are the official Miniclip website, Mobile9, Phoneky, or any other reputable website that offers safe and reliable downloads.
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diff --git a/spaces/1phancelerku/anime-remove-background/Free Download IGNOU Solved Assignment for M.Com 2023 All Subjects and Languages.md b/spaces/1phancelerku/anime-remove-background/Free Download IGNOU Solved Assignment for M.Com 2023 All Subjects and Languages.md
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index 841ebd7d07c2d256d6a662f9baf4caceada9f1d9..0000000000000000000000000000000000000000
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IGNOU Solved Assignment Free Download M.Com: A Complete Guide
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If you are pursuing a Master of Commerce (M.Com) course from Indira Gandhi National Open University (IGNOU), you might be wondering how to get the solved assignments for your course. Solved assignments are important for completing your course and getting good marks in the exams. In this article, we will tell you everything you need to know about IGNOU solved assignment free download M.Com, including what is the M.Com course, why do you need solved assignments, and how to download them easily.
IGNOU M.Com course is a two-year postgraduate program that offers a comprehensive and advanced study of various aspects of commerce, such as accounting, finance, marketing, management, economics, and taxation. The course aims to develop the skills and knowledge of the students in the field of commerce and prepare them for various career opportunities in the public and private sectors.
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Eligibility Criteria
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To be eligible for admission to IGNOU M.Com course, you need to have a bachelor's degree or equivalent in any discipline from a recognized university. You also need to have at least 50% marks in aggregate or equivalent grade point average (GPA). However, there is a relaxation of 5% marks for SC/ST/OBC/PWD candidates.
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Course Structure
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The IGNOU M.Com course consists of 12 courses, out of which six are compulsory and six are elective. The compulsory courses cover the core subjects of commerce, such as business environment, financial management, marketing management, organizational behavior, and research methodology. The elective courses allow the students to choose from various specializations, such as accounting and finance, banking and insurance, business policy and corporate governance, international business operations, and management accounting and financial strategies.
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Course Fee
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The total fee for IGNOU M.Com course is Rs. 13,200/-, which is payable in two installments of Rs. 6,600/- each. The fee includes the registration fee, examination fee, study material fee, and other charges. The fee can be paid online through debit card/credit card/net banking or offline through demand draft/bank challan.
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Why Do You Need IGNOU Solved Assignments?
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IGNOU solved assignments are an essential part of your M.Com course. They are written assignments that you need to submit to your study center before the due date. They carry 30% weightage in your final marks and help you to improve your understanding of the course content.
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How to Submit Solved Assignments
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Solve the assignments by referring to the study material and using your own words and examples.
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Write your name, enrollment number, course code, course title, assignment code, study center code, and date on the first page of each assignment.
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Make sure that your handwriting is neat and legible and that you follow the word limit and format specified in the assignment guidelines.
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Attach a copy of the assignment submission form with each assignment and keep a copy of the assignments and the form for your reference.
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Submit your assignments to your study center coordinator before the last date of submission.
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How to Download IGNOU Solved Assignments for M.Com?
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If you are looking for IGNOU solved assignments for M.Com, you can download them for free from various online sources. However, you should be careful about the quality and authenticity of the solved assignments and use them only as a reference and not as a substitute for your own work. To download IGNOU solved assignments for M.Com, you can follow these steps:
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Visit the Official Website of IGNOU
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The first step is to visit the official website of IGNOU at www.ignou.ac.in. Here, you can find the latest updates and notifications regarding the M.Com course and the assignments. You can also access the study material and other resources for your course.
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Select Your Course and Session
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Download the Solved Assignments in PDF Format
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The final step is to download the solved assignments in PDF format from the links provided on the same page. You can find the solved assignments for both compulsory and elective courses for each session. You can also download the assignment questions and guidelines from here. You can save the solved assignments on your device or print them out for your convenience.
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197e85843d
-
-
\ No newline at end of file
diff --git a/spaces/1toTree/lora_test/ppdiffusers/pipelines/pndm/__init__.py b/spaces/1toTree/lora_test/ppdiffusers/pipelines/pndm/__init__.py
deleted file mode 100644
index 132f733020f3f30109687fc5e7b1bd53ac83eed1..0000000000000000000000000000000000000000
--- a/spaces/1toTree/lora_test/ppdiffusers/pipelines/pndm/__init__.py
+++ /dev/null
@@ -1,17 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-# Copyright 2022 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# flake8: noqa
-from .pipeline_pndm import PNDMPipeline
diff --git a/spaces/AIFILMS/generate_human_motion/VQ-Trans/dataset/prepare/download_glove.sh b/spaces/AIFILMS/generate_human_motion/VQ-Trans/dataset/prepare/download_glove.sh
deleted file mode 100644
index 058599aa32c9c97e0e3fc0a9658822e9c904955a..0000000000000000000000000000000000000000
--- a/spaces/AIFILMS/generate_human_motion/VQ-Trans/dataset/prepare/download_glove.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-echo -e "Downloading glove (in use by the evaluators)"
-gdown --fuzzy https://drive.google.com/file/d/1bCeS6Sh_mLVTebxIgiUHgdPrroW06mb6/view?usp=sharing
-rm -rf glove
-
-unzip glove.zip
-echo -e "Cleaning\n"
-rm glove.zip
-
-echo -e "Downloading done!"
\ No newline at end of file
diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_speech/utils/audio/__init__.py b/spaces/AIGC-Audio/AudioGPT/text_to_speech/utils/audio/__init__.py
deleted file mode 100644
index 737ddaad53b77109b1edc05015e41bfde7651476..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/text_to_speech/utils/audio/__init__.py
+++ /dev/null
@@ -1,82 +0,0 @@
-import librosa
-import numpy as np
-import pyloudnorm as pyln
-
-from text_to_speech.utils.audio.vad import trim_long_silences
-
-
-def librosa_pad_lr(x, fsize, fshift, pad_sides=1):
- '''compute right padding (final frame) or both sides padding (first and final frames)
- '''
- assert pad_sides in (1, 2)
- # return int(fsize // 2)
- pad = (x.shape[0] // fshift + 1) * fshift - x.shape[0]
- if pad_sides == 1:
- return 0, pad
- else:
- return pad // 2, pad // 2 + pad % 2
-
-
-def amp_to_db(x):
- return 20 * np.log10(np.maximum(1e-5, x))
-
-
-def db_to_amp(x):
- return 10.0 ** (x * 0.05)
-
-
-def normalize(S, min_level_db):
- return (S - min_level_db) / -min_level_db
-
-
-def denormalize(D, min_level_db):
- return (D * -min_level_db) + min_level_db
-
-
-def librosa_wav2spec(wav_path,
- fft_size=1024,
- hop_size=256,
- win_length=1024,
- window="hann",
- num_mels=80,
- fmin=80,
- fmax=-1,
- eps=1e-6,
- sample_rate=22050,
- loud_norm=False,
- trim_long_sil=False):
- if isinstance(wav_path, str):
- if trim_long_sil:
- wav, _, _ = trim_long_silences(wav_path, sample_rate)
- else:
- wav, _ = librosa.core.load(wav_path, sr=sample_rate)
- else:
- wav = wav_path
-
- if loud_norm:
- meter = pyln.Meter(sample_rate) # create BS.1770 meter
- loudness = meter.integrated_loudness(wav)
- wav = pyln.normalize.loudness(wav, loudness, -22.0)
- if np.abs(wav).max() > 1:
- wav = wav / np.abs(wav).max()
-
- # get amplitude spectrogram
- x_stft = librosa.stft(wav, n_fft=fft_size, hop_length=hop_size,
- win_length=win_length, window=window, pad_mode="constant")
- linear_spc = np.abs(x_stft) # (n_bins, T)
-
- # get mel basis
- fmin = 0 if fmin == -1 else fmin
- fmax = sample_rate / 2 if fmax == -1 else fmax
- mel_basis = librosa.filters.mel(sample_rate, fft_size, num_mels, fmin, fmax)
-
- # calculate mel spec
- mel = mel_basis @ linear_spc
- mel = np.log10(np.maximum(eps, mel)) # (n_mel_bins, T)
- l_pad, r_pad = librosa_pad_lr(wav, fft_size, hop_size, 1)
- wav = np.pad(wav, (l_pad, r_pad), mode='constant', constant_values=0.0)
- wav = wav[:mel.shape[1] * hop_size]
-
- # log linear spec
- linear_spc = np.log10(np.maximum(eps, linear_spc))
- return {'wav': wav, 'mel': mel.T, 'linear': linear_spc.T, 'mel_basis': mel_basis}
diff --git a/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/diffusionmodules/custom_openaimodel.py b/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/diffusionmodules/custom_openaimodel.py
deleted file mode 100644
index 4412eac52c294266dee21680f698b10a4614b4fa..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/diffusionmodules/custom_openaimodel.py
+++ /dev/null
@@ -1,368 +0,0 @@
-from abc import abstractmethod
-from functools import partial
-import math
-from typing import Iterable
-
-import numpy as np
-import torch as th
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ldm.modules.diffusionmodules.util import (
- checkpoint,
- conv_nd,
- linear,
- avg_pool_nd,
- zero_module,
- normalization,
- timestep_embedding,
-)
-from ldm.modules.attention import SpatialTransformer
-from ldm.modules.diffusionmodules.openaimodel import convert_module_to_f16, convert_module_to_f32, AttentionPool2d, \
- TimestepBlock, TimestepEmbedSequential, Upsample, TransposedUpsample, Downsample, ResBlock, AttentionBlock, count_flops_attn, \
- QKVAttentionLegacy, QKVAttention
-
-
-class UNetModel(nn.Module):
- """
- The full UNet model with attention and timestep embedding.
- :param in_channels: channels in the input Tensor.
- :param model_channels: base channel count for the model.
- :param out_channels: channels in the output Tensor.
- :param num_res_blocks: number of residual blocks per downsample.
- :param attention_resolutions: a collection of downsample rates at which
- attention will take place. May be a set, list, or tuple.
- For example, if this contains 4, then at 4x downsampling, attention
- will be used.
- :param dropout: the dropout probability.
- :param channel_mult: channel multiplier for each level of the UNet.
- :param conv_resample: if True, use learned convolutions for upsampling and
- downsampling.
- :param dims: determines if the signal is 1D, 2D, or 3D.
- :param num_classes: if specified (as an int), then this model will be
- class-conditional with `num_classes` classes.
- :param use_checkpoint: use gradient checkpointing to reduce memory usage.
- :param num_heads: the number of attention heads in each attention layer.
- :param num_heads_channels: if specified, ignore num_heads and instead use
- a fixed channel width per attention head.
- :param num_heads_upsample: works with num_heads to set a different number
- of heads for upsampling. Deprecated.
- :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
- :param resblock_updown: use residual blocks for up/downsampling.
- :param use_new_attention_order: use a different attention pattern for potentially
- increased efficiency.
- """
-
- def __init__(
- self,
- image_size,
- in_channels,
- model_channels,
- out_channels,
- num_res_blocks,
- attention_resolutions,
- dropout=0,
- channel_mult=(1, 2, 4, 8),
- conv_resample=True,
- dims=2,
- num_classes=None,
- use_checkpoint=False,
- use_fp16=False,
- num_heads=-1,
- num_head_channels=-1,
- num_heads_upsample=-1,
- use_scale_shift_norm=False,
- resblock_updown=False,
- use_new_attention_order=False,
- use_spatial_transformer=False, # custom transformer support
- transformer_depth=1, # custom transformer support
- context_dim=None, # custom transformer support
- n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model
- legacy=True,
- use_context_project=False, # custom text to audio support
- use_context_attn=True # custom text to audio support
- ):
- super().__init__()
- if use_spatial_transformer:
- assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
-
- if context_dim is not None and not use_context_project:
- assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
- from omegaconf.listconfig import ListConfig
- if type(context_dim) == ListConfig:
- context_dim = list(context_dim)
-
- if num_heads_upsample == -1:
- num_heads_upsample = num_heads
-
- if num_heads == -1:
- assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
-
- if num_head_channels == -1:
- assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
-
- self.image_size = image_size
- self.in_channels = in_channels
- self.model_channels = model_channels
- self.out_channels = out_channels
- self.num_res_blocks = num_res_blocks
- self.attention_resolutions = attention_resolutions
- self.dropout = dropout
- self.channel_mult = channel_mult
- self.conv_resample = conv_resample
- self.num_classes = num_classes
- self.use_checkpoint = use_checkpoint
- self.dtype = th.float16 if use_fp16 else th.float32
- self.num_heads = num_heads
- self.num_head_channels = num_head_channels
- self.num_heads_upsample = num_heads_upsample
- self.predict_codebook_ids = n_embed is not None
-
- time_embed_dim = model_channels * 4
- self.time_embed = nn.Sequential(
- linear(model_channels, time_embed_dim),
- nn.SiLU(),
- linear(time_embed_dim, time_embed_dim),
- )
-
- if self.num_classes is not None:
- self.label_emb = nn.Embedding(num_classes, time_embed_dim)
-
- self.input_blocks = nn.ModuleList(
- [
- TimestepEmbedSequential(
- conv_nd(dims, in_channels, model_channels, 3, padding=1)
- )
- ]
- )
- self._feature_size = model_channels
- input_block_chans = [model_channels]
- ch = model_channels
- ds = 1
- for level, mult in enumerate(channel_mult):
- for _ in range(num_res_blocks):
- layers = [
- ResBlock(
- ch,
- time_embed_dim,
- dropout,
- out_channels=mult * model_channels,
- dims=dims,
- use_checkpoint=use_checkpoint,
- use_scale_shift_norm=use_scale_shift_norm,
- )
- ]
- ch = mult * model_channels
- if ds in attention_resolutions:
- if num_head_channels == -1:
- dim_head = ch // num_heads
- else:
- num_heads = ch // num_head_channels
- dim_head = num_head_channels
- if legacy:
- #num_heads = 1
- dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
- layers.append(
- AttentionBlock(
- ch,
- use_checkpoint=use_checkpoint,
- num_heads=num_heads,
- num_head_channels=dim_head,
- use_new_attention_order=use_new_attention_order,
- ) if not use_spatial_transformer else SpatialTransformer(
- ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
- )
- )
- self.input_blocks.append(TimestepEmbedSequential(*layers))
- self._feature_size += ch
- input_block_chans.append(ch)
- if level != len(channel_mult) - 1:
- out_ch = ch
- self.input_blocks.append(
- TimestepEmbedSequential(
- ResBlock(
- ch,
- time_embed_dim,
- dropout,
- out_channels=out_ch,
- dims=dims,
- use_checkpoint=use_checkpoint,
- use_scale_shift_norm=use_scale_shift_norm,
- down=True,
- )
- if resblock_updown
- else Downsample(
- ch, conv_resample, dims=dims, out_channels=out_ch
- )
- )
- )
- ch = out_ch
- input_block_chans.append(ch)
- ds *= 2
- self._feature_size += ch
-
- if num_head_channels == -1:
- dim_head = ch // num_heads
- else:
- num_heads = ch // num_head_channels
- dim_head = num_head_channels
- if legacy:
- #num_heads = 1
- dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
- self.middle_block = TimestepEmbedSequential(
- ResBlock(
- ch,
- time_embed_dim,
- dropout,
- dims=dims,
- use_checkpoint=use_checkpoint,
- use_scale_shift_norm=use_scale_shift_norm,
- ),
- AttentionBlock(
- ch,
- use_checkpoint=use_checkpoint,
- num_heads=num_heads,
- num_head_channels=dim_head,
- use_new_attention_order=use_new_attention_order,
- ) if not use_spatial_transformer else SpatialTransformer(
- ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
- ),
- ResBlock(
- ch,
- time_embed_dim,
- dropout,
- dims=dims,
- use_checkpoint=use_checkpoint,
- use_scale_shift_norm=use_scale_shift_norm,
- ),
- )
- self._feature_size += ch
-
- self.output_blocks = nn.ModuleList([])
- for level, mult in list(enumerate(channel_mult))[::-1]:
- for i in range(num_res_blocks + 1):
- ich = input_block_chans.pop()
- layers = [
- ResBlock(
- ch + ich,
- time_embed_dim,
- dropout,
- out_channels=model_channels * mult,
- dims=dims,
- use_checkpoint=use_checkpoint,
- use_scale_shift_norm=use_scale_shift_norm,
- )
- ]
- ch = model_channels * mult
- if ds in attention_resolutions:
- if num_head_channels == -1:
- dim_head = ch // num_heads
- else:
- num_heads = ch // num_head_channels
- dim_head = num_head_channels
- if legacy:
- #num_heads = 1
- dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
- layers.append(
- AttentionBlock(
- ch,
- use_checkpoint=use_checkpoint,
- num_heads=num_heads_upsample,
- num_head_channels=dim_head,
- use_new_attention_order=use_new_attention_order,
- ) if not use_spatial_transformer else SpatialTransformer(
- ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
- )
- )
- if level and i == num_res_blocks:
- out_ch = ch
- layers.append(
- ResBlock(
- ch,
- time_embed_dim,
- dropout,
- out_channels=out_ch,
- dims=dims,
- use_checkpoint=use_checkpoint,
- use_scale_shift_norm=use_scale_shift_norm,
- up=True,
- )
- if resblock_updown
- else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
- )
- ds //= 2
- self.output_blocks.append(TimestepEmbedSequential(*layers))
- self._feature_size += ch
-
- self.out = nn.Sequential(
- normalization(ch),
- nn.SiLU(),
- zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
- )
- if self.predict_codebook_ids:
- self.id_predictor = nn.Sequential(
- normalization(ch),
- conv_nd(dims, model_channels, n_embed, 1),
- #nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits
- )
-
- self.use_context_project = use_context_project
- if use_context_project:
- self.context_project = linear(context_dim, time_embed_dim)
- self.use_context_attn = use_context_attn
-
-
- def convert_to_fp16(self):
- """
- Convert the torso of the model to float16.
- """
- self.input_blocks.apply(convert_module_to_f16)
- self.middle_block.apply(convert_module_to_f16)
- self.output_blocks.apply(convert_module_to_f16)
-
- def convert_to_fp32(self):
- """
- Convert the torso of the model to float32.
- """
- self.input_blocks.apply(convert_module_to_f32)
- self.middle_block.apply(convert_module_to_f32)
- self.output_blocks.apply(convert_module_to_f32)
-
- def forward(self, x, timesteps=None, context=None, y=None,**kwargs):
- """
- Apply the model to an input batch.
- :param x: an [N x C x ...] Tensor of inputs.
- :param timesteps: a 1-D batch of timesteps.
- :param context: conditioning plugged in via crossattn
- :param y: an [N] Tensor of labels, if class-conditional.
- :return: an [N x C x ...] Tensor of outputs.
- """
- assert (y is not None) == (
- self.num_classes is not None
- ), "must specify y if and only if the model is class-conditional"
- hs = []
- t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
- emb = self.time_embed(t_emb)
-
- if self.num_classes is not None:
- assert y.shape == (x.shape[0],)
- emb = emb + self.label_emb(y)
-
- # For text-to-audio using global CLIP
- if self.use_context_project:
- context = self.context_project(context)
- emb = emb + context.squeeze(1)
-
- h = x.type(self.dtype)
- for module in self.input_blocks:
- h = module(h, emb, context if self.use_context_attn else None)
- hs.append(h)
- h = self.middle_block(h, emb, context if self.use_context_attn else None)
- for module in self.output_blocks:
- h = th.cat([h, hs.pop()], dim=1)
- h = module(h, emb, context if self.use_context_attn else None)
- h = h.type(x.dtype)
- if self.predict_codebook_ids:
- return self.id_predictor(h)
- else:
- return self.out(h)
diff --git a/spaces/AbandonedMuse/UnlimitedMusicGen/audiocraft/modules/lstm.py b/spaces/AbandonedMuse/UnlimitedMusicGen/audiocraft/modules/lstm.py
deleted file mode 100644
index c0866175950c1ca4f6cca98649525e6481853bba..0000000000000000000000000000000000000000
--- a/spaces/AbandonedMuse/UnlimitedMusicGen/audiocraft/modules/lstm.py
+++ /dev/null
@@ -1,25 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from torch import nn
-
-
-class StreamableLSTM(nn.Module):
- """LSTM without worrying about the hidden state, nor the layout of the data.
- Expects input as convolutional layout.
- """
- def __init__(self, dimension: int, num_layers: int = 2, skip: bool = True):
- super().__init__()
- self.skip = skip
- self.lstm = nn.LSTM(dimension, dimension, num_layers)
-
- def forward(self, x):
- x = x.permute(2, 0, 1)
- y, _ = self.lstm(x)
- if self.skip:
- y = y + x
- y = y.permute(1, 2, 0)
- return y
diff --git a/spaces/AgentVerse/agentVerse/agentverse/output_parser/__init__.py b/spaces/AgentVerse/agentVerse/agentverse/output_parser/__init__.py
deleted file mode 100644
index 8b54c8edd671343e85508b1e33e8d40c12415cb8..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/agentverse/output_parser/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from agentverse.registry import Registry
-
-output_parser_registry = Registry(name="OutputParserRegistry")
-
-from .output_parser import *
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/lineprogresscanvas/LineProgressCanvas.d.ts b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/lineprogresscanvas/LineProgressCanvas.d.ts
deleted file mode 100644
index 7d855d01aa179ebe72cd5319c2ba17858eb86634..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/lineprogresscanvas/LineProgressCanvas.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-import LineProgressCanvas from "../../../plugins/lineprogresscanvas";
-export default LineProgressCanvas;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/perspective/Factory.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/perspective/Factory.js
deleted file mode 100644
index 9abb53984852529f5041ee26646e4b77b741539a..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/perspective/Factory.js
+++ /dev/null
@@ -1,11 +0,0 @@
-import Perspective from './Perspective.js';
-import ObjectFactory from '../ObjectFactory.js';
-import SetValue from '../../../plugins/utils/object/SetValue.js';
-
-ObjectFactory.register('perspective', function (gameObject, config) {
- return new Perspective(gameObject, config);
-});
-
-SetValue(window, 'RexPlugins.UI.Perspective', Perspective);
-
-export default Perspective;
\ No newline at end of file
diff --git a/spaces/Alexxggs/ggvpnewen/README.md b/spaces/Alexxggs/ggvpnewen/README.md
deleted file mode 100644
index d0822e29f6bc0a7e3313ed49e11dba1d20fd1f05..0000000000000000000000000000000000000000
--- a/spaces/Alexxggs/ggvpnewen/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Ggvpnewen
-emoji: ⚡
-colorFrom: gray
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.9
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/AlgoveraAI/ocean-marketplace/README.md b/spaces/AlgoveraAI/ocean-marketplace/README.md
deleted file mode 100644
index d40fd60e032ea96989b9571b4eabbac346750755..0000000000000000000000000000000000000000
--- a/spaces/AlgoveraAI/ocean-marketplace/README.md
+++ /dev/null
@@ -1,46 +0,0 @@
----
-title: Ocean Marketplace
-emoji: 🧺
-colorFrom: indigo
-colorTo: yellow
-sdk: gradio
-app_file: app.py
-pinned: false
-license: mit
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`models`: _List[string]_
-HF model IDs (like "gpt2" or "deepset/roberta-base-squad2") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`datasets`: _List[string]_
-HF dataset IDs (like "common_voice" or "oscar-corpus/OSCAR-2109") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/Alpaca233/SadTalker/scripts/download_models.sh b/spaces/Alpaca233/SadTalker/scripts/download_models.sh
deleted file mode 100644
index 6898648b153a2826557693dabb5adaf13bee2645..0000000000000000000000000000000000000000
--- a/spaces/Alpaca233/SadTalker/scripts/download_models.sh
+++ /dev/null
@@ -1,32 +0,0 @@
-mkdir ./checkpoints
-
-# lagency download link
-# wget -nc https://github.com/Winfredy/SadTalker/releases/download/v0.0.2/auido2exp_00300-model.pth -O ./checkpoints/auido2exp_00300-model.pth
-# wget -nc https://github.com/Winfredy/SadTalker/releases/download/v0.0.2/auido2pose_00140-model.pth -O ./checkpoints/auido2pose_00140-model.pth
-# wget -nc https://github.com/Winfredy/SadTalker/releases/download/v0.0.2/epoch_20.pth -O ./checkpoints/epoch_20.pth
-# wget -nc https://github.com/Winfredy/SadTalker/releases/download/v0.0.2/facevid2vid_00189-model.pth.tar -O ./checkpoints/facevid2vid_00189-model.pth.tar
-# wget -nc https://github.com/Winfredy/SadTalker/releases/download/v0.0.2/shape_predictor_68_face_landmarks.dat -O ./checkpoints/shape_predictor_68_face_landmarks.dat
-# wget -nc https://github.com/Winfredy/SadTalker/releases/download/v0.0.2/wav2lip.pth -O ./checkpoints/wav2lip.pth
-# wget -nc https://github.com/Winfredy/SadTalker/releases/download/v0.0.2/mapping_00229-model.pth.tar -O ./checkpoints/mapping_00229-model.pth.tar
-# wget -nc https://github.com/Winfredy/SadTalker/releases/download/v0.0.2/mapping_00109-model.pth.tar -O ./checkpoints/mapping_00109-model.pth.tar
-# wget -nc https://github.com/Winfredy/SadTalker/releases/download/v0.0.2/hub.zip -O ./checkpoints/hub.zip
-# unzip -n ./checkpoints/hub.zip -d ./checkpoints/
-
-
-#### download the new links.
-wget -nc https://github.com/OpenTalker/SadTalker/releases/download/v0.0.2-rc/mapping_00109-model.pth.tar -O ./checkpoints/mapping_00109-model.pth.tar
-wget -nc https://github.com/OpenTalker/SadTalker/releases/download/v0.0.2-rc/mapping_00229-model.pth.tar -O ./checkpoints/mapping_00229-model.pth.tar
-wget -nc https://github.com/OpenTalker/SadTalker/releases/download/v0.0.2-rc/SadTalker_V0.0.2_256.safetensors -O ./checkpoints/SadTalker_V0.0.2_256.safetensors
-wget -nc https://github.com/OpenTalker/SadTalker/releases/download/v0.0.2-rc/SadTalker_V0.0.2_512.safetensors -O ./checkpoints/SadTalker_V0.0.2_512.safetensors
-
-
-# wget -nc https://github.com/Winfredy/SadTalker/releases/download/v0.0.2/BFM_Fitting.zip -O ./checkpoints/BFM_Fitting.zip
-# unzip -n ./checkpoints/BFM_Fitting.zip -d ./checkpoints/
-
-### enhancer
-mkdir -p ./gfpgan/weights
-wget -nc https://github.com/xinntao/facexlib/releases/download/v0.1.0/alignment_WFLW_4HG.pth -O ./gfpgan/weights/alignment_WFLW_4HG.pth
-wget -nc https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth -O ./gfpgan/weights/detection_Resnet50_Final.pth
-wget -nc https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -O ./gfpgan/weights/GFPGANv1.4.pth
-wget -nc https://github.com/xinntao/facexlib/releases/download/v0.2.2/parsing_parsenet.pth -O ./gfpgan/weights/parsing_parsenet.pth
-
diff --git a/spaces/Amrrs/DragGan-Inversion/stylegan_human/pti/training/projectors/w_projector.py b/spaces/Amrrs/DragGan-Inversion/stylegan_human/pti/training/projectors/w_projector.py
deleted file mode 100644
index 12553b8c4450dc8bb605b0eab0f55d90ba2d051f..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/DragGan-Inversion/stylegan_human/pti/training/projectors/w_projector.py
+++ /dev/null
@@ -1,161 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto. Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Project given image to the latent space of pretrained network pickle."""
-
-import copy
-import wandb
-import numpy as np
-import torch
-import torch.nn.functional as F
-from tqdm import tqdm
-from pti.pti_configs import global_config, hyperparameters
-from utils import log_utils
-import dnnlib
-
-
-def project(
- G,
- # [C,H,W] and dynamic range [0,255], W & H must match G output resolution
- target: torch.Tensor,
- *,
- num_steps=1000,
- w_avg_samples=10000,
- initial_learning_rate=0.01,
- initial_noise_factor=0.05,
- lr_rampdown_length=0.25,
- lr_rampup_length=0.05,
- noise_ramp_length=0.75,
- regularize_noise_weight=1e5,
- verbose=False,
- device: torch.device,
- use_wandb=False,
- initial_w=None,
- image_log_step=global_config.image_rec_result_log_snapshot,
- w_name: str
-):
- print(target.shape, G.img_channels, G.img_resolution, G.img_resolution//2)
- assert target.shape == (
- G.img_channels, G.img_resolution, G.img_resolution // 2)
-
- def logprint(*args):
- if verbose:
- print(*args)
-
- G = copy.deepcopy(G).eval().requires_grad_(
- False).to(device).float() # type: ignore
-
- # Compute w stats.
- logprint(
- f'Computing W midpoint and stddev using {w_avg_samples} samples...')
- z_samples = np.random.RandomState(123).randn(w_avg_samples, G.z_dim)
- w_samples = G.mapping(torch.from_numpy(
- z_samples).to(device), None) # [N, L, C]
- w_samples = w_samples[:, :1, :].cpu(
- ).numpy().astype(np.float32) # [N, 1, C]
- w_avg = np.mean(w_samples, axis=0, keepdims=True) # [1, 1, C]
- w_avg_tensor = torch.from_numpy(w_avg).to(global_config.device)
- w_std = (np.sum((w_samples - w_avg) ** 2) / w_avg_samples) ** 0.5
-
- start_w = initial_w if initial_w is not None else w_avg
-
- # Setup noise inputs.
- noise_bufs = {name: buf for (
- name, buf) in G.synthesis.named_buffers() if 'noise_const' in name}
-
- # Load VGG16 feature detector.
- url = 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/vgg16.pt'
- with dnnlib.util.open_url(url) as f:
- vgg16 = torch.jit.load(f).eval().to(device)
-
- # Features for target image.
- target_images = target.unsqueeze(0).to(device).to(torch.float32)
- if target_images.shape[2] > 256:
- target_images = F.interpolate(
- target_images, size=(256, 256), mode='area')
- target_features = vgg16(
- target_images, resize_images=False, return_lpips=True)
-
- w_opt = torch.tensor(start_w, dtype=torch.float32, device=device,
- requires_grad=True) # pylint: disable=not-callable
- optimizer = torch.optim.Adam([w_opt] + list(noise_bufs.values()), betas=(0.9, 0.999),
- lr=hyperparameters.first_inv_lr)
-
- # Init noise.
- for buf in noise_bufs.values():
- buf[:] = torch.randn_like(buf)
- buf.requires_grad = True
-
- for step in range(num_steps):
-
- # Learning rate schedule.
- t = step / num_steps
- w_noise_scale = w_std * initial_noise_factor * \
- max(0.0, 1.0 - t / noise_ramp_length) ** 2
- lr_ramp = min(1.0, (1.0 - t) / lr_rampdown_length)
- lr_ramp = 0.5 - 0.5 * np.cos(lr_ramp * np.pi)
- lr_ramp = lr_ramp * min(1.0, t / lr_rampup_length)
- lr = initial_learning_rate * lr_ramp
- for param_group in optimizer.param_groups:
- param_group['lr'] = lr
-
- # Synth images from opt_w.
- w_noise = torch.randn_like(w_opt) * w_noise_scale
- ws = (w_opt + w_noise).repeat([1, G.mapping.num_ws, 1])
- synth_images = G.synthesis(ws, noise_mode='const', force_fp32=True)
-
- # Downsample image to 256x256 if it's larger than that. VGG was built for 224x224 images.
- synth_images = (synth_images + 1) * (255 / 2)
- if synth_images.shape[2] > 256:
- synth_images = F.interpolate(
- synth_images, size=(256, 256), mode='area')
-
- # Features for synth images.
- synth_features = vgg16(
- synth_images, resize_images=False, return_lpips=True)
- dist = (target_features - synth_features).square().sum()
-
- # Noise regularization.
- reg_loss = 0.0
- for v in noise_bufs.values():
- noise = v[None, None, :, :] # must be [1,1,H,W] for F.avg_pool2d()
- while True:
- reg_loss += (noise * torch.roll(noise,
- shifts=1, dims=3)).mean() ** 2
- reg_loss += (noise * torch.roll(noise,
- shifts=1, dims=2)).mean() ** 2
- if noise.shape[2] <= 8:
- break
- noise = F.avg_pool2d(noise, kernel_size=2)
- loss = dist + reg_loss * regularize_noise_weight
- if step % 10 == 0:
- print("project loss", step, loss.data)
- if step % image_log_step == 0:
- with torch.no_grad():
- if use_wandb:
- global_config.training_step += 1
- wandb.log({f'first projection _{w_name}': loss.detach(
- ).cpu()}, step=global_config.training_step)
- log_utils.log_image_from_w(w_opt.repeat(
- [1, G.mapping.num_ws, 1]), G, w_name)
-
- # Step
- optimizer.zero_grad(set_to_none=True)
- loss.backward()
- optimizer.step()
- logprint(
- f'step {step + 1:>4d}/{num_steps}: dist {dist:<4.2f} loss {float(loss):<5.2f}')
-
- # Normalize noise.
- with torch.no_grad():
- for buf in noise_bufs.values():
- buf -= buf.mean()
- buf *= buf.square().mean().rsqrt()
-
- del G
- return w_opt.repeat([1, 18, 1])
diff --git a/spaces/Amrrs/DragGan-Inversion/torch_utils/training_stats.py b/spaces/Amrrs/DragGan-Inversion/torch_utils/training_stats.py
deleted file mode 100644
index aa5837c2948372ecdb3e34076f4b3f4f42c81fef..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/DragGan-Inversion/torch_utils/training_stats.py
+++ /dev/null
@@ -1,283 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto. Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Facilities for reporting and collecting training statistics across
-multiple processes and devices. The interface is designed to minimize
-synchronization overhead as well as the amount of boilerplate in user
-code."""
-
-import re
-import numpy as np
-import torch
-import dnnlib
-
-from . import misc
-
-# ----------------------------------------------------------------------------
-
-_num_moments = 3 # [num_scalars, sum_of_scalars, sum_of_squares]
-# Data type to use for initial per-tensor reduction.
-_reduce_dtype = torch.float32
-_counter_dtype = torch.float64 # Data type to use for the internal counters.
-_rank = 0 # Rank of the current process.
-# Device to use for multiprocess communication. None = single-process.
-_sync_device = None
-_sync_called = False # Has _sync() been called yet?
-# Running counters on each device, updated by report(): name => device => torch.Tensor
-_counters = dict()
-# Cumulative counters on the CPU, updated by _sync(): name => torch.Tensor
-_cumulative = dict()
-
-# ----------------------------------------------------------------------------
-
-
-def init_multiprocessing(rank, sync_device):
- r"""Initializes `torch_utils.training_stats` for collecting statistics
- across multiple processes.
-
- This function must be called after
- `torch.distributed.init_process_group()` and before `Collector.update()`.
- The call is not necessary if multi-process collection is not needed.
-
- Args:
- rank: Rank of the current process.
- sync_device: PyTorch device to use for inter-process
- communication, or None to disable multi-process
- collection. Typically `torch.device('cuda', rank)`.
- """
- global _rank, _sync_device
- assert not _sync_called
- _rank = rank
- _sync_device = sync_device
-
-# ----------------------------------------------------------------------------
-
-
-@misc.profiled_function
-def report(name, value):
- r"""Broadcasts the given set of scalars to all interested instances of
- `Collector`, across device and process boundaries.
-
- This function is expected to be extremely cheap and can be safely
- called from anywhere in the training loop, loss function, or inside a
- `torch.nn.Module`.
-
- Warning: The current implementation expects the set of unique names to
- be consistent across processes. Please make sure that `report()` is
- called at least once for each unique name by each process, and in the
- same order. If a given process has no scalars to broadcast, it can do
- `report(name, [])` (empty list).
-
- Args:
- name: Arbitrary string specifying the name of the statistic.
- Averages are accumulated separately for each unique name.
- value: Arbitrary set of scalars. Can be a list, tuple,
- NumPy array, PyTorch tensor, or Python scalar.
-
- Returns:
- The same `value` that was passed in.
- """
- if name not in _counters:
- _counters[name] = dict()
-
- elems = torch.as_tensor(value)
- if elems.numel() == 0:
- return value
-
- elems = elems.detach().flatten().to(_reduce_dtype)
- moments = torch.stack([
- torch.ones_like(elems).sum(),
- elems.sum(),
- elems.square().sum(),
- ])
- assert moments.ndim == 1 and moments.shape[0] == _num_moments
- moments = moments.to(_counter_dtype)
-
- device = moments.device
- if device not in _counters[name]:
- _counters[name][device] = torch.zeros_like(moments)
- _counters[name][device].add_(moments)
- return value
-
-# ----------------------------------------------------------------------------
-
-
-def report0(name, value):
- r"""Broadcasts the given set of scalars by the first process (`rank = 0`),
- but ignores any scalars provided by the other processes.
- See `report()` for further details.
- """
- report(name, value if _rank == 0 else [])
- return value
-
-# ----------------------------------------------------------------------------
-
-
-class Collector:
- r"""Collects the scalars broadcasted by `report()` and `report0()` and
- computes their long-term averages (mean and standard deviation) over
- user-defined periods of time.
-
- The averages are first collected into internal counters that are not
- directly visible to the user. They are then copied to the user-visible
- state as a result of calling `update()` and can then be queried using
- `mean()`, `std()`, `as_dict()`, etc. Calling `update()` also resets the
- internal counters for the next round, so that the user-visible state
- effectively reflects averages collected between the last two calls to
- `update()`.
-
- Args:
- regex: Regular expression defining which statistics to
- collect. The default is to collect everything.
- keep_previous: Whether to retain the previous averages if no
- scalars were collected on a given round
- (default: True).
- """
-
- def __init__(self, regex='.*', keep_previous=True):
- self._regex = re.compile(regex)
- self._keep_previous = keep_previous
- self._cumulative = dict()
- self._moments = dict()
- self.update()
- self._moments.clear()
-
- def names(self):
- r"""Returns the names of all statistics broadcasted so far that
- match the regular expression specified at construction time.
- """
- return [name for name in _counters if self._regex.fullmatch(name)]
-
- def update(self):
- r"""Copies current values of the internal counters to the
- user-visible state and resets them for the next round.
-
- If `keep_previous=True` was specified at construction time, the
- operation is skipped for statistics that have received no scalars
- since the last update, retaining their previous averages.
-
- This method performs a number of GPU-to-CPU transfers and one
- `torch.distributed.all_reduce()`. It is intended to be called
- periodically in the main training loop, typically once every
- N training steps.
- """
- if not self._keep_previous:
- self._moments.clear()
- for name, cumulative in _sync(self.names()):
- if name not in self._cumulative:
- self._cumulative[name] = torch.zeros(
- [_num_moments], dtype=_counter_dtype)
- delta = cumulative - self._cumulative[name]
- self._cumulative[name].copy_(cumulative)
- if float(delta[0]) != 0:
- self._moments[name] = delta
-
- def _get_delta(self, name):
- r"""Returns the raw moments that were accumulated for the given
- statistic between the last two calls to `update()`, or zero if
- no scalars were collected.
- """
- assert self._regex.fullmatch(name)
- if name not in self._moments:
- self._moments[name] = torch.zeros(
- [_num_moments], dtype=_counter_dtype)
- return self._moments[name]
-
- def num(self, name):
- r"""Returns the number of scalars that were accumulated for the given
- statistic between the last two calls to `update()`, or zero if
- no scalars were collected.
- """
- delta = self._get_delta(name)
- return int(delta[0])
-
- def mean(self, name):
- r"""Returns the mean of the scalars that were accumulated for the
- given statistic between the last two calls to `update()`, or NaN if
- no scalars were collected.
- """
- delta = self._get_delta(name)
- if int(delta[0]) == 0:
- return float('nan')
- return float(delta[1] / delta[0])
-
- def std(self, name):
- r"""Returns the standard deviation of the scalars that were
- accumulated for the given statistic between the last two calls to
- `update()`, or NaN if no scalars were collected.
- """
- delta = self._get_delta(name)
- if int(delta[0]) == 0 or not np.isfinite(float(delta[1])):
- return float('nan')
- if int(delta[0]) == 1:
- return float(0)
- mean = float(delta[1] / delta[0])
- raw_var = float(delta[2] / delta[0])
- return np.sqrt(max(raw_var - np.square(mean), 0))
-
- def as_dict(self):
- r"""Returns the averages accumulated between the last two calls to
- `update()` as an `dnnlib.EasyDict`. The contents are as follows:
-
- dnnlib.EasyDict(
- NAME = dnnlib.EasyDict(num=FLOAT, mean=FLOAT, std=FLOAT),
- ...
- )
- """
- stats = dnnlib.EasyDict()
- for name in self.names():
- stats[name] = dnnlib.EasyDict(num=self.num(
- name), mean=self.mean(name), std=self.std(name))
- return stats
-
- def __getitem__(self, name):
- r"""Convenience getter.
- `collector[name]` is a synonym for `collector.mean(name)`.
- """
- return self.mean(name)
-
-# ----------------------------------------------------------------------------
-
-
-def _sync(names):
- r"""Synchronize the global cumulative counters across devices and
- processes. Called internally by `Collector.update()`.
- """
- if len(names) == 0:
- return []
- global _sync_called
- _sync_called = True
-
- # Collect deltas within current rank.
- deltas = []
- device = _sync_device if _sync_device is not None else torch.device('cpu')
- for name in names:
- delta = torch.zeros(
- [_num_moments], dtype=_counter_dtype, device=device)
- for counter in _counters[name].values():
- delta.add_(counter.to(device))
- counter.copy_(torch.zeros_like(counter))
- deltas.append(delta)
- deltas = torch.stack(deltas)
-
- # Sum deltas across ranks.
- if _sync_device is not None:
- torch.distributed.all_reduce(deltas)
-
- # Update cumulative values.
- deltas = deltas.cpu()
- for idx, name in enumerate(names):
- if name not in _cumulative:
- _cumulative[name] = torch.zeros(
- [_num_moments], dtype=_counter_dtype)
- _cumulative[name].add_(deltas[idx])
-
- # Return name-value pairs.
- return [(name, _cumulative[name]) for name in names]
-
-# ----------------------------------------------------------------------------
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/research_projects/colossalai/train_dreambooth_colossalai.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/research_projects/colossalai/train_dreambooth_colossalai.py
deleted file mode 100644
index 3d4466bf94b74c5b324b970913c142342871cf78..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/research_projects/colossalai/train_dreambooth_colossalai.py
+++ /dev/null
@@ -1,673 +0,0 @@
-import argparse
-import hashlib
-import math
-import os
-from pathlib import Path
-
-import colossalai
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint
-from colossalai.context.parallel_mode import ParallelMode
-from colossalai.core import global_context as gpc
-from colossalai.logging import disable_existing_loggers, get_dist_logger
-from colossalai.nn.optimizer.gemini_optimizer import GeminiAdamOptimizer
-from colossalai.nn.parallel.utils import get_static_torch_model
-from colossalai.utils import get_current_device
-from colossalai.utils.model.colo_init_context import ColoInitContext
-from huggingface_hub import create_repo, upload_folder
-from PIL import Image
-from torch.utils.data import Dataset
-from torchvision import transforms
-from tqdm.auto import tqdm
-from transformers import AutoTokenizer, PretrainedConfig
-
-from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel
-from diffusers.optimization import get_scheduler
-
-
-disable_existing_loggers()
-logger = get_dist_logger()
-
-
-def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str):
- text_encoder_config = PretrainedConfig.from_pretrained(
- pretrained_model_name_or_path,
- subfolder="text_encoder",
- revision=args.revision,
- )
- model_class = text_encoder_config.architectures[0]
-
- if model_class == "CLIPTextModel":
- from transformers import CLIPTextModel
-
- return CLIPTextModel
- elif model_class == "RobertaSeriesModelWithTransformation":
- from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation
-
- return RobertaSeriesModelWithTransformation
- else:
- raise ValueError(f"{model_class} is not supported.")
-
-
-def parse_args(input_args=None):
- parser = argparse.ArgumentParser(description="Simple example of a training script.")
- parser.add_argument(
- "--pretrained_model_name_or_path",
- type=str,
- default=None,
- required=True,
- help="Path to pretrained model or model identifier from huggingface.co/models.",
- )
- parser.add_argument(
- "--revision",
- type=str,
- default=None,
- required=False,
- help="Revision of pretrained model identifier from huggingface.co/models.",
- )
- parser.add_argument(
- "--tokenizer_name",
- type=str,
- default=None,
- help="Pretrained tokenizer name or path if not the same as model_name",
- )
- parser.add_argument(
- "--instance_data_dir",
- type=str,
- default=None,
- required=True,
- help="A folder containing the training data of instance images.",
- )
- parser.add_argument(
- "--class_data_dir",
- type=str,
- default=None,
- required=False,
- help="A folder containing the training data of class images.",
- )
- parser.add_argument(
- "--instance_prompt",
- type=str,
- default="a photo of sks dog",
- required=False,
- help="The prompt with identifier specifying the instance",
- )
- parser.add_argument(
- "--class_prompt",
- type=str,
- default=None,
- help="The prompt to specify images in the same class as provided instance images.",
- )
- parser.add_argument(
- "--with_prior_preservation",
- default=False,
- action="store_true",
- help="Flag to add prior preservation loss.",
- )
- parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
- parser.add_argument(
- "--num_class_images",
- type=int,
- default=100,
- help=(
- "Minimal class images for prior preservation loss. If there are not enough images already present in"
- " class_data_dir, additional images will be sampled with class_prompt."
- ),
- )
- parser.add_argument(
- "--output_dir",
- type=str,
- default="text-inversion-model",
- help="The output directory where the model predictions and checkpoints will be written.",
- )
- parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
- parser.add_argument(
- "--resolution",
- type=int,
- default=512,
- help=(
- "The resolution for input images, all the images in the train/validation dataset will be resized to this"
- " resolution"
- ),
- )
- parser.add_argument(
- "--placement",
- type=str,
- default="cpu",
- help="Placement Policy for Gemini. Valid when using colossalai as dist plan.",
- )
- parser.add_argument(
- "--center_crop",
- default=False,
- action="store_true",
- help=(
- "Whether to center crop the input images to the resolution. If not set, the images will be randomly"
- " cropped. The images will be resized to the resolution first before cropping."
- ),
- )
- parser.add_argument(
- "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
- )
- parser.add_argument(
- "--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
- )
- parser.add_argument("--num_train_epochs", type=int, default=1)
- parser.add_argument(
- "--max_train_steps",
- type=int,
- default=None,
- help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
- )
- parser.add_argument("--save_steps", type=int, default=500, help="Save checkpoint every X updates steps.")
- parser.add_argument(
- "--gradient_checkpointing",
- action="store_true",
- help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
- )
- parser.add_argument(
- "--learning_rate",
- type=float,
- default=5e-6,
- help="Initial learning rate (after the potential warmup period) to use.",
- )
- parser.add_argument(
- "--scale_lr",
- action="store_true",
- default=False,
- help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
- )
- parser.add_argument(
- "--lr_scheduler",
- type=str,
- default="constant",
- help=(
- 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
- ' "constant", "constant_with_warmup"]'
- ),
- )
- parser.add_argument(
- "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
- )
- parser.add_argument(
- "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
- )
-
- parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
- parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
- parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
- parser.add_argument(
- "--hub_model_id",
- type=str,
- default=None,
- help="The name of the repository to keep in sync with the local `output_dir`.",
- )
- parser.add_argument(
- "--logging_dir",
- type=str,
- default="logs",
- help=(
- "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
- " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
- ),
- )
- parser.add_argument(
- "--mixed_precision",
- type=str,
- default=None,
- choices=["no", "fp16", "bf16"],
- help=(
- "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
- " 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
- " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
- ),
- )
- parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
-
- if input_args is not None:
- args = parser.parse_args(input_args)
- else:
- args = parser.parse_args()
-
- env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
- if env_local_rank != -1 and env_local_rank != args.local_rank:
- args.local_rank = env_local_rank
-
- if args.with_prior_preservation:
- if args.class_data_dir is None:
- raise ValueError("You must specify a data directory for class images.")
- if args.class_prompt is None:
- raise ValueError("You must specify prompt for class images.")
- else:
- if args.class_data_dir is not None:
- logger.warning("You need not use --class_data_dir without --with_prior_preservation.")
- if args.class_prompt is not None:
- logger.warning("You need not use --class_prompt without --with_prior_preservation.")
-
- return args
-
-
-class DreamBoothDataset(Dataset):
- """
- A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
- It pre-processes the images and the tokenizes prompts.
- """
-
- def __init__(
- self,
- instance_data_root,
- instance_prompt,
- tokenizer,
- class_data_root=None,
- class_prompt=None,
- size=512,
- center_crop=False,
- ):
- self.size = size
- self.center_crop = center_crop
- self.tokenizer = tokenizer
-
- self.instance_data_root = Path(instance_data_root)
- if not self.instance_data_root.exists():
- raise ValueError("Instance images root doesn't exists.")
-
- self.instance_images_path = list(Path(instance_data_root).iterdir())
- self.num_instance_images = len(self.instance_images_path)
- self.instance_prompt = instance_prompt
- self._length = self.num_instance_images
-
- if class_data_root is not None:
- self.class_data_root = Path(class_data_root)
- self.class_data_root.mkdir(parents=True, exist_ok=True)
- self.class_images_path = list(self.class_data_root.iterdir())
- self.num_class_images = len(self.class_images_path)
- self._length = max(self.num_class_images, self.num_instance_images)
- self.class_prompt = class_prompt
- else:
- self.class_data_root = None
-
- self.image_transforms = transforms.Compose(
- [
- transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
- transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
- transforms.ToTensor(),
- transforms.Normalize([0.5], [0.5]),
- ]
- )
-
- def __len__(self):
- return self._length
-
- def __getitem__(self, index):
- example = {}
- instance_image = Image.open(self.instance_images_path[index % self.num_instance_images])
- if not instance_image.mode == "RGB":
- instance_image = instance_image.convert("RGB")
- example["instance_images"] = self.image_transforms(instance_image)
- example["instance_prompt_ids"] = self.tokenizer(
- self.instance_prompt,
- padding="do_not_pad",
- truncation=True,
- max_length=self.tokenizer.model_max_length,
- ).input_ids
-
- if self.class_data_root:
- class_image = Image.open(self.class_images_path[index % self.num_class_images])
- if not class_image.mode == "RGB":
- class_image = class_image.convert("RGB")
- example["class_images"] = self.image_transforms(class_image)
- example["class_prompt_ids"] = self.tokenizer(
- self.class_prompt,
- padding="do_not_pad",
- truncation=True,
- max_length=self.tokenizer.model_max_length,
- ).input_ids
-
- return example
-
-
-class PromptDataset(Dataset):
- "A simple dataset to prepare the prompts to generate class images on multiple GPUs."
-
- def __init__(self, prompt, num_samples):
- self.prompt = prompt
- self.num_samples = num_samples
-
- def __len__(self):
- return self.num_samples
-
- def __getitem__(self, index):
- example = {}
- example["prompt"] = self.prompt
- example["index"] = index
- return example
-
-
-# Gemini + ZeRO DDP
-def gemini_zero_dpp(model: torch.nn.Module, placememt_policy: str = "auto"):
- from colossalai.nn.parallel import GeminiDDP
-
- model = GeminiDDP(
- model, device=get_current_device(), placement_policy=placememt_policy, pin_memory=True, search_range_mb=64
- )
- return model
-
-
-def main(args):
- if args.seed is None:
- colossalai.launch_from_torch(config={})
- else:
- colossalai.launch_from_torch(config={}, seed=args.seed)
-
- local_rank = gpc.get_local_rank(ParallelMode.DATA)
- world_size = gpc.get_world_size(ParallelMode.DATA)
-
- if args.with_prior_preservation:
- class_images_dir = Path(args.class_data_dir)
- if not class_images_dir.exists():
- class_images_dir.mkdir(parents=True)
- cur_class_images = len(list(class_images_dir.iterdir()))
-
- if cur_class_images < args.num_class_images:
- torch_dtype = torch.float16 if get_current_device() == "cuda" else torch.float32
- pipeline = DiffusionPipeline.from_pretrained(
- args.pretrained_model_name_or_path,
- torch_dtype=torch_dtype,
- safety_checker=None,
- revision=args.revision,
- )
- pipeline.set_progress_bar_config(disable=True)
-
- num_new_images = args.num_class_images - cur_class_images
- logger.info(f"Number of class images to sample: {num_new_images}.")
-
- sample_dataset = PromptDataset(args.class_prompt, num_new_images)
- sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)
-
- pipeline.to(get_current_device())
-
- for example in tqdm(
- sample_dataloader,
- desc="Generating class images",
- disable=not local_rank == 0,
- ):
- images = pipeline(example["prompt"]).images
-
- for i, image in enumerate(images):
- hash_image = hashlib.sha1(image.tobytes()).hexdigest()
- image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
- image.save(image_filename)
-
- del pipeline
-
- # Handle the repository creation
- if local_rank == 0:
- if args.output_dir is not None:
- os.makedirs(args.output_dir, exist_ok=True)
-
- if args.push_to_hub:
- repo_id = create_repo(
- repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
- ).repo_id
-
- # Load the tokenizer
- if args.tokenizer_name:
- logger.info(f"Loading tokenizer from {args.tokenizer_name}", ranks=[0])
- tokenizer = AutoTokenizer.from_pretrained(
- args.tokenizer_name,
- revision=args.revision,
- use_fast=False,
- )
- elif args.pretrained_model_name_or_path:
- logger.info("Loading tokenizer from pretrained model", ranks=[0])
- tokenizer = AutoTokenizer.from_pretrained(
- args.pretrained_model_name_or_path,
- subfolder="tokenizer",
- revision=args.revision,
- use_fast=False,
- )
- # import correct text encoder class
- text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path)
-
- # Load models and create wrapper for stable diffusion
-
- logger.info(f"Loading text_encoder from {args.pretrained_model_name_or_path}", ranks=[0])
-
- text_encoder = text_encoder_cls.from_pretrained(
- args.pretrained_model_name_or_path,
- subfolder="text_encoder",
- revision=args.revision,
- )
-
- logger.info(f"Loading AutoencoderKL from {args.pretrained_model_name_or_path}", ranks=[0])
- vae = AutoencoderKL.from_pretrained(
- args.pretrained_model_name_or_path,
- subfolder="vae",
- revision=args.revision,
- )
-
- logger.info(f"Loading UNet2DConditionModel from {args.pretrained_model_name_or_path}", ranks=[0])
- with ColoInitContext(device=get_current_device()):
- unet = UNet2DConditionModel.from_pretrained(
- args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, low_cpu_mem_usage=False
- )
-
- vae.requires_grad_(False)
- text_encoder.requires_grad_(False)
-
- if args.gradient_checkpointing:
- unet.enable_gradient_checkpointing()
-
- if args.scale_lr:
- args.learning_rate = args.learning_rate * args.train_batch_size * world_size
-
- unet = gemini_zero_dpp(unet, args.placement)
-
- # config optimizer for colossalai zero
- optimizer = GeminiAdamOptimizer(
- unet, lr=args.learning_rate, initial_scale=2**5, clipping_norm=args.max_grad_norm
- )
-
- # load noise_scheduler
- noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
-
- # prepare dataset
- logger.info(f"Prepare dataset from {args.instance_data_dir}", ranks=[0])
- train_dataset = DreamBoothDataset(
- instance_data_root=args.instance_data_dir,
- instance_prompt=args.instance_prompt,
- class_data_root=args.class_data_dir if args.with_prior_preservation else None,
- class_prompt=args.class_prompt,
- tokenizer=tokenizer,
- size=args.resolution,
- center_crop=args.center_crop,
- )
-
- def collate_fn(examples):
- input_ids = [example["instance_prompt_ids"] for example in examples]
- pixel_values = [example["instance_images"] for example in examples]
-
- # Concat class and instance examples for prior preservation.
- # We do this to avoid doing two forward passes.
- if args.with_prior_preservation:
- input_ids += [example["class_prompt_ids"] for example in examples]
- pixel_values += [example["class_images"] for example in examples]
-
- pixel_values = torch.stack(pixel_values)
- pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
-
- input_ids = tokenizer.pad(
- {"input_ids": input_ids},
- padding="max_length",
- max_length=tokenizer.model_max_length,
- return_tensors="pt",
- ).input_ids
-
- batch = {
- "input_ids": input_ids,
- "pixel_values": pixel_values,
- }
- return batch
-
- train_dataloader = torch.utils.data.DataLoader(
- train_dataset, batch_size=args.train_batch_size, shuffle=True, collate_fn=collate_fn, num_workers=1
- )
-
- # Scheduler and math around the number of training steps.
- overrode_max_train_steps = False
- num_update_steps_per_epoch = math.ceil(len(train_dataloader))
- if args.max_train_steps is None:
- args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
- overrode_max_train_steps = True
-
- lr_scheduler = get_scheduler(
- args.lr_scheduler,
- optimizer=optimizer,
- num_warmup_steps=args.lr_warmup_steps,
- num_training_steps=args.max_train_steps,
- )
- weight_dtype = torch.float32
- if args.mixed_precision == "fp16":
- weight_dtype = torch.float16
- elif args.mixed_precision == "bf16":
- weight_dtype = torch.bfloat16
-
- # Move text_encode and vae to gpu.
- # For mixed precision training we cast the text_encoder and vae weights to half-precision
- # as these models are only used for inference, keeping weights in full precision is not required.
- vae.to(get_current_device(), dtype=weight_dtype)
- text_encoder.to(get_current_device(), dtype=weight_dtype)
-
- # We need to recalculate our total training steps as the size of the training dataloader may have changed.
- num_update_steps_per_epoch = math.ceil(len(train_dataloader))
- if overrode_max_train_steps:
- args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
- # Afterwards we recalculate our number of training epochs
- args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
-
- # Train!
- total_batch_size = args.train_batch_size * world_size
-
- logger.info("***** Running training *****", ranks=[0])
- logger.info(f" Num examples = {len(train_dataset)}", ranks=[0])
- logger.info(f" Num batches each epoch = {len(train_dataloader)}", ranks=[0])
- logger.info(f" Num Epochs = {args.num_train_epochs}", ranks=[0])
- logger.info(f" Instantaneous batch size per device = {args.train_batch_size}", ranks=[0])
- logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}", ranks=[0])
- logger.info(f" Total optimization steps = {args.max_train_steps}", ranks=[0])
-
- # Only show the progress bar once on each machine.
- progress_bar = tqdm(range(args.max_train_steps), disable=not local_rank == 0)
- progress_bar.set_description("Steps")
- global_step = 0
-
- torch.cuda.synchronize()
- for epoch in range(args.num_train_epochs):
- unet.train()
- for step, batch in enumerate(train_dataloader):
- torch.cuda.reset_peak_memory_stats()
- # Move batch to gpu
- for key, value in batch.items():
- batch[key] = value.to(get_current_device(), non_blocking=True)
-
- # Convert images to latent space
- optimizer.zero_grad()
-
- latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample()
- latents = latents * 0.18215
-
- # Sample noise that we'll add to the latents
- noise = torch.randn_like(latents)
- bsz = latents.shape[0]
- # Sample a random timestep for each image
- timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
- timesteps = timesteps.long()
-
- # Add noise to the latents according to the noise magnitude at each timestep
- # (this is the forward diffusion process)
- noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
-
- # Get the text embedding for conditioning
- encoder_hidden_states = text_encoder(batch["input_ids"])[0]
-
- # Predict the noise residual
- model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
-
- # Get the target for loss depending on the prediction type
- if noise_scheduler.config.prediction_type == "epsilon":
- target = noise
- elif noise_scheduler.config.prediction_type == "v_prediction":
- target = noise_scheduler.get_velocity(latents, noise, timesteps)
- else:
- raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
-
- if args.with_prior_preservation:
- # Chunk the noise and model_pred into two parts and compute the loss on each part separately.
- model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
- target, target_prior = torch.chunk(target, 2, dim=0)
-
- # Compute instance loss
- loss = F.mse_loss(model_pred.float(), target.float(), reduction="none").mean([1, 2, 3]).mean()
-
- # Compute prior loss
- prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean")
-
- # Add the prior loss to the instance loss.
- loss = loss + args.prior_loss_weight * prior_loss
- else:
- loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
-
- optimizer.backward(loss)
-
- optimizer.step()
- lr_scheduler.step()
- logger.info(f"max GPU_mem cost is {torch.cuda.max_memory_allocated()/2**20} MB", ranks=[0])
- # Checks if the accelerator has performed an optimization step behind the scenes
- progress_bar.update(1)
- global_step += 1
- logs = {
- "loss": loss.detach().item(),
- "lr": optimizer.param_groups[0]["lr"],
- } # lr_scheduler.get_last_lr()[0]}
- progress_bar.set_postfix(**logs)
-
- if global_step % args.save_steps == 0:
- torch.cuda.synchronize()
- torch_unet = get_static_torch_model(unet)
- if local_rank == 0:
- pipeline = DiffusionPipeline.from_pretrained(
- args.pretrained_model_name_or_path,
- unet=torch_unet,
- revision=args.revision,
- )
- save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
- pipeline.save_pretrained(save_path)
- logger.info(f"Saving model checkpoint to {save_path}", ranks=[0])
- if global_step >= args.max_train_steps:
- break
-
- torch.cuda.synchronize()
- unet = get_static_torch_model(unet)
-
- if local_rank == 0:
- pipeline = DiffusionPipeline.from_pretrained(
- args.pretrained_model_name_or_path,
- unet=unet,
- revision=args.revision,
- )
-
- pipeline.save_pretrained(args.output_dir)
- logger.info(f"Saving model checkpoint to {args.output_dir}", ranks=[0])
-
- if args.push_to_hub:
- upload_folder(
- repo_id=repo_id,
- folder_path=args.output_dir,
- commit_message="End of training",
- ignore_patterns=["step_*", "epoch_*"],
- )
-
-
-if __name__ == "__main__":
- args = parse_args()
- main(args)
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/scripts/convert_unclip_txt2img_to_image_variation.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/scripts/convert_unclip_txt2img_to_image_variation.py
deleted file mode 100644
index 07f8ebf2a3d012600a533dcfa642b609c31a3d8c..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/scripts/convert_unclip_txt2img_to_image_variation.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import argparse
-
-from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
-
-from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
-
- parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
-
- parser.add_argument(
- "--txt2img_unclip",
- default="kakaobrain/karlo-v1-alpha",
- type=str,
- required=False,
- help="The pretrained txt2img unclip.",
- )
-
- args = parser.parse_args()
-
- txt2img = UnCLIPPipeline.from_pretrained(args.txt2img_unclip)
-
- feature_extractor = CLIPImageProcessor()
- image_encoder = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")
-
- img2img = UnCLIPImageVariationPipeline(
- decoder=txt2img.decoder,
- text_encoder=txt2img.text_encoder,
- tokenizer=txt2img.tokenizer,
- text_proj=txt2img.text_proj,
- feature_extractor=feature_extractor,
- image_encoder=image_encoder,
- super_res_first=txt2img.super_res_first,
- super_res_last=txt2img.super_res_last,
- decoder_scheduler=txt2img.decoder_scheduler,
- super_res_scheduler=txt2img.super_res_scheduler,
- )
-
- img2img.save_pretrained(args.dump_path)
diff --git a/spaces/Andy1621/UniFormerV2_mit_demo/uniformerv2.py b/spaces/Andy1621/UniFormerV2_mit_demo/uniformerv2.py
deleted file mode 100644
index 5ca7c3d511f4e3c2c8c6e89ace89e2ad8680d34f..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/UniFormerV2_mit_demo/uniformerv2.py
+++ /dev/null
@@ -1,510 +0,0 @@
-#!/usr/bin/env python
-import os
-from collections import OrderedDict
-
-from timm.models.layers import DropPath
-import torch
-from torch import nn
-from torch.nn import MultiheadAttention
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-
-
-MODEL_PATH = './'
-_MODELS = {
- "ViT-B/16": os.path.join(MODEL_PATH, "vit_b16.pth"),
- "ViT-L/14": os.path.join(MODEL_PATH, "vit_l14.pth"),
- "ViT-L/14_336": os.path.join(MODEL_PATH, "vit_l14_336.pth"),
-}
-
-
-class LayerNorm(nn.LayerNorm):
- """Subclass torch's LayerNorm to handle fp16."""
-
- def forward(self, x):
- orig_type = x.dtype
- ret = super().forward(x.type(torch.float32))
- return ret.type(orig_type)
-
-
-class QuickGELU(nn.Module):
- def forward(self, x):
- return x * torch.sigmoid(1.702 * x)
-
-
-class Local_MHRA(nn.Module):
- def __init__(self, d_model, dw_reduction=1.5, pos_kernel_size=3):
- super().__init__()
-
- padding = pos_kernel_size // 2
- re_d_model = int(d_model // dw_reduction)
- self.pos_embed = nn.Sequential(
- nn.BatchNorm3d(d_model),
- nn.Conv3d(d_model, re_d_model, kernel_size=1, stride=1, padding=0),
- nn.Conv3d(re_d_model, re_d_model, kernel_size=(pos_kernel_size, 1, 1), stride=(1, 1, 1), padding=(padding, 0, 0), groups=re_d_model),
- nn.Conv3d(re_d_model, d_model, kernel_size=1, stride=1, padding=0),
- )
-
- # init zero
- print('Init zero for Conv in pos_emb')
- nn.init.constant_(self.pos_embed[3].weight, 0)
- nn.init.constant_(self.pos_embed[3].bias, 0)
-
- def forward(self, x):
- return self.pos_embed(x)
-
-
-class ResidualAttentionBlock(nn.Module):
- def __init__(
- self, d_model, n_head, attn_mask=None, drop_path=0.0,
- dw_reduction=1.5, no_lmhra=False, double_lmhra=True
- ):
- super().__init__()
-
- self.n_head = n_head
- self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
- print(f'Drop path rate: {drop_path}')
-
- self.no_lmhra = no_lmhra
- self.double_lmhra = double_lmhra
- print(f'No L_MHRA: {no_lmhra}')
- print(f'Double L_MHRA: {double_lmhra}')
- if not no_lmhra:
- self.lmhra1 = Local_MHRA(d_model, dw_reduction=dw_reduction)
- if double_lmhra:
- self.lmhra2 = Local_MHRA(d_model, dw_reduction=dw_reduction)
-
- # spatial
- self.attn = MultiheadAttention(d_model, n_head)
- self.ln_1 = LayerNorm(d_model)
- self.mlp = nn.Sequential(OrderedDict([
- ("c_fc", nn.Linear(d_model, d_model * 4)),
- ("gelu", QuickGELU()),
- ("c_proj", nn.Linear(d_model * 4, d_model))
- ]))
- self.ln_2 = LayerNorm(d_model)
- self.attn_mask = attn_mask
-
- def attention(self, x):
- self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
- return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
-
- def forward(self, x, T=8, use_checkpoint=False):
- # x: 1+HW, NT, C
- if not self.no_lmhra:
- # Local MHRA
- tmp_x = x[1:, :, :]
- L, NT, C = tmp_x.shape
- N = NT // T
- H = W = int(L ** 0.5)
- tmp_x = tmp_x.view(H, W, N, T, C).permute(2, 4, 3, 0, 1).contiguous()
- tmp_x = tmp_x + self.drop_path(self.lmhra1(tmp_x))
- tmp_x = tmp_x.view(N, C, T, L).permute(3, 0, 2, 1).contiguous().view(L, NT, C)
- x = torch.cat([x[:1, :, :], tmp_x], dim=0)
- # MHSA
- if use_checkpoint:
- attn_out = checkpoint.checkpoint(self.attention, self.ln_1(x))
- x = x + self.drop_path(attn_out)
- else:
- x = x + self.drop_path(self.attention(self.ln_1(x)))
- # Local MHRA
- if not self.no_lmhra and self.double_lmhra:
- tmp_x = x[1:, :, :]
- tmp_x = tmp_x.view(H, W, N, T, C).permute(2, 4, 3, 0, 1).contiguous()
- tmp_x = tmp_x + self.drop_path(self.lmhra2(tmp_x))
- tmp_x = tmp_x.view(N, C, T, L).permute(3, 0, 2, 1).contiguous().view(L, NT, C)
- x = torch.cat([x[:1, :, :], tmp_x], dim=0)
- # FFN
- if use_checkpoint:
- mlp_out = checkpoint.checkpoint(self.mlp, self.ln_2(x))
- x = x + self.drop_path(mlp_out)
- else:
- x = x + self.drop_path(self.mlp(self.ln_2(x)))
- return x
-
-
-class Extractor(nn.Module):
- def __init__(
- self, d_model, n_head, attn_mask=None,
- mlp_factor=4.0, dropout=0.0, drop_path=0.0,
- ):
- super().__init__()
-
- self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
- print(f'Drop path rate: {drop_path}')
- self.attn = nn.MultiheadAttention(d_model, n_head)
- self.ln_1 = nn.LayerNorm(d_model)
- d_mlp = round(mlp_factor * d_model)
- self.mlp = nn.Sequential(OrderedDict([
- ("c_fc", nn.Linear(d_model, d_mlp)),
- ("gelu", QuickGELU()),
- ("dropout", nn.Dropout(dropout)),
- ("c_proj", nn.Linear(d_mlp, d_model))
- ]))
- self.ln_2 = nn.LayerNorm(d_model)
- self.ln_3 = nn.LayerNorm(d_model)
- self.attn_mask = attn_mask
-
- # zero init
- nn.init.xavier_uniform_(self.attn.in_proj_weight)
- nn.init.constant_(self.attn.out_proj.weight, 0.)
- nn.init.constant_(self.attn.out_proj.bias, 0.)
- nn.init.xavier_uniform_(self.mlp[0].weight)
- nn.init.constant_(self.mlp[-1].weight, 0.)
- nn.init.constant_(self.mlp[-1].bias, 0.)
-
- def attention(self, x, y):
- d_model = self.ln_1.weight.size(0)
- q = (x @ self.attn.in_proj_weight[:d_model].T) + self.attn.in_proj_bias[:d_model]
-
- k = (y @ self.attn.in_proj_weight[d_model:-d_model].T) + self.attn.in_proj_bias[d_model:-d_model]
- v = (y @ self.attn.in_proj_weight[-d_model:].T) + self.attn.in_proj_bias[-d_model:]
- Tx, Ty, N = q.size(0), k.size(0), q.size(1)
- q = q.view(Tx, N, self.attn.num_heads, self.attn.head_dim).permute(1, 2, 0, 3)
- k = k.view(Ty, N, self.attn.num_heads, self.attn.head_dim).permute(1, 2, 0, 3)
- v = v.view(Ty, N, self.attn.num_heads, self.attn.head_dim).permute(1, 2, 0, 3)
- aff = (q @ k.transpose(-2, -1) / (self.attn.head_dim ** 0.5))
-
- aff = aff.softmax(dim=-1)
- out = aff @ v
- out = out.permute(2, 0, 1, 3).flatten(2)
- out = self.attn.out_proj(out)
- return out
-
- def forward(self, x, y):
- x = x + self.drop_path(self.attention(self.ln_1(x), self.ln_3(y)))
- x = x + self.drop_path(self.mlp(self.ln_2(x)))
- return x
-
-
-class Transformer(nn.Module):
- def __init__(
- self, width, layers, heads, attn_mask=None, backbone_drop_path_rate=0.,
- use_checkpoint=False, checkpoint_num=[0], t_size=8, dw_reduction=2,
- no_lmhra=False, double_lmhra=True,
- return_list=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
- n_layers=12, n_dim=768, n_head=12, mlp_factor=4.0, drop_path_rate=0.,
- mlp_dropout=[0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5],
- cls_dropout=0.5, num_classes=400,
- ):
- super().__init__()
- self.T = t_size
- self.return_list = return_list
- # backbone
- b_dpr = [x.item() for x in torch.linspace(0, backbone_drop_path_rate, layers)]
- self.resblocks = nn.ModuleList([
- ResidualAttentionBlock(
- width, heads, attn_mask,
- drop_path=b_dpr[i],
- dw_reduction=dw_reduction,
- no_lmhra=no_lmhra,
- double_lmhra=double_lmhra,
- ) for i in range(layers)
- ])
- # checkpoint
- self.use_checkpoint = use_checkpoint
- self.checkpoint_num = checkpoint_num
- self.n_layers = n_layers
- print(f'Use checkpoint: {self.use_checkpoint}')
- print(f'Checkpoint number: {self.checkpoint_num}')
-
- # global block
- assert n_layers == len(return_list)
- if n_layers > 0:
- self.temporal_cls_token = nn.Parameter(torch.zeros(1, 1, n_dim))
- self.dpe = nn.ModuleList([
- nn.Conv3d(n_dim, n_dim, kernel_size=3, stride=1, padding=1, bias=True, groups=n_dim)
- for i in range(n_layers)
- ])
- for m in self.dpe:
- nn.init.constant_(m.bias, 0.)
- dpr = [x.item() for x in torch.linspace(0, drop_path_rate, n_layers)]
- self.dec = nn.ModuleList([
- Extractor(
- n_dim, n_head, mlp_factor=mlp_factor,
- dropout=mlp_dropout[i], drop_path=dpr[i],
- ) for i in range(n_layers)
- ])
- self.balance = nn.Parameter(torch.zeros((n_dim)))
- self.sigmoid = nn.Sigmoid()
- # projection
- self.proj = nn.Sequential(
- nn.LayerNorm(n_dim),
- nn.Dropout(cls_dropout),
- nn.Linear(n_dim, num_classes),
- )
-
- def forward(self, x):
- T_down = self.T
- L, NT, C = x.shape
- N = NT // T_down
- H = W = int((L - 1) ** 0.5)
-
- if self.n_layers > 0:
- cls_token = self.temporal_cls_token.repeat(1, N, 1)
-
- j = -1
- for i, resblock in enumerate(self.resblocks):
- if self.use_checkpoint and i < self.checkpoint_num[0]:
- x = resblock(x, self.T, use_checkpoint=True)
- else:
- x = resblock(x, T_down)
- if i in self.return_list:
- j += 1
- tmp_x = x.clone()
- tmp_x = tmp_x.view(L, N, T_down, C)
- # dpe
- _, tmp_feats = tmp_x[:1], tmp_x[1:]
- tmp_feats = tmp_feats.permute(1, 3, 2, 0).reshape(N, C, T_down, H, W)
- tmp_feats = self.dpe[j](tmp_feats).view(N, C, T_down, L - 1).permute(3, 0, 2, 1).contiguous()
- tmp_x[1:] = tmp_x[1:] + tmp_feats
- # global block
- tmp_x = tmp_x.permute(2, 0, 1, 3).flatten(0, 1) # T * L, N, C
- cls_token = self.dec[j](cls_token, tmp_x)
-
- if self.n_layers > 0:
- weight = self.sigmoid(self.balance)
- residual = x.view(L, N, T_down, C)[0].mean(1) # L, N, T, C
- return self.proj((1 - weight) * cls_token[0, :, :] + weight * residual)
- else:
- residual = x.view(L, N, T_down, C)[0].mean(1) # L, N, T, C
- return self.proj(residual)
-
-
-class VisionTransformer(nn.Module):
- def __init__(
- self,
- # backbone
- input_resolution, patch_size, width, layers, heads, output_dim, backbone_drop_path_rate=0.,
- use_checkpoint=False, checkpoint_num=[0], t_size=8, kernel_size=3, dw_reduction=1.5,
- temporal_downsample=True,
- no_lmhra=-False, double_lmhra=True,
- # global block
- return_list=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
- n_layers=12, n_dim=768, n_head=12, mlp_factor=4.0, drop_path_rate=0.,
- mlp_dropout=[0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5],
- cls_dropout=0.5, num_classes=400,
- ):
- super().__init__()
- self.input_resolution = input_resolution
- self.output_dim = output_dim
- padding = (kernel_size - 1) // 2
- if temporal_downsample:
- self.conv1 = nn.Conv3d(3, width, (kernel_size, patch_size, patch_size), (2, patch_size, patch_size), (padding, 0, 0), bias=False)
- t_size = t_size // 2
- else:
- self.conv1 = nn.Conv3d(3, width, (1, patch_size, patch_size), (1, patch_size, patch_size), (0, 0, 0), bias=False)
-
- scale = width ** -0.5
- self.class_embedding = nn.Parameter(scale * torch.randn(width))
- self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width))
- self.ln_pre = LayerNorm(width)
-
- self.transformer = Transformer(
- width, layers, heads, dw_reduction=dw_reduction,
- backbone_drop_path_rate=backbone_drop_path_rate,
- use_checkpoint=use_checkpoint, checkpoint_num=checkpoint_num, t_size=t_size,
- no_lmhra=no_lmhra, double_lmhra=double_lmhra,
- return_list=return_list, n_layers=n_layers, n_dim=n_dim, n_head=n_head,
- mlp_factor=mlp_factor, drop_path_rate=drop_path_rate, mlp_dropout=mlp_dropout,
- cls_dropout=cls_dropout, num_classes=num_classes,
- )
-
- def forward(self, x):
- x = self.conv1(x) # shape = [*, width, grid, grid]
- N, C, T, H, W = x.shape
- x = x.permute(0, 2, 3, 4, 1).reshape(N * T, H * W, C)
-
- x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1) # shape = [*, grid ** 2 + 1, width]
- x = x + self.positional_embedding.to(x.dtype)
- x = self.ln_pre(x)
-
- x = x.permute(1, 0, 2) # NLD -> LND
- out = self.transformer(x)
- return out
-
-
-def inflate_weight(weight_2d, time_dim, center=True):
- print(f'Init center: {center}')
- if center:
- weight_3d = torch.zeros(*weight_2d.shape)
- weight_3d = weight_3d.unsqueeze(2).repeat(1, 1, time_dim, 1, 1)
- middle_idx = time_dim // 2
- weight_3d[:, :, middle_idx, :, :] = weight_2d
- else:
- weight_3d = weight_2d.unsqueeze(2).repeat(1, 1, time_dim, 1, 1)
- weight_3d = weight_3d / time_dim
- return weight_3d
-
-
-def load_state_dict(model, state_dict):
- state_dict_3d = model.state_dict()
- for k in state_dict.keys():
- if state_dict[k].shape != state_dict_3d[k].shape:
- if len(state_dict_3d[k].shape) <= 2:
- print(f'Ignore: {k}')
- continue
- print(f'Inflate: {k}, {state_dict[k].shape} => {state_dict_3d[k].shape}')
- time_dim = state_dict_3d[k].shape[2]
- state_dict[k] = inflate_weight(state_dict[k], time_dim)
- model.load_state_dict(state_dict, strict=False)
-
-
-def uniformerv2_b16(
- pretrained=True, use_checkpoint=False, checkpoint_num=[0],
- t_size=16, dw_reduction=1.5, backbone_drop_path_rate=0.,
- temporal_downsample=True,
- no_lmhra=False, double_lmhra=True,
- return_list=[8, 9, 10, 11],
- n_layers=4, n_dim=768, n_head=12, mlp_factor=4.0, drop_path_rate=0.,
- mlp_dropout=[0.5, 0.5, 0.5, 0.5],
- cls_dropout=0.5, num_classes=400,
-):
- model = VisionTransformer(
- input_resolution=224,
- patch_size=16,
- width=768,
- layers=12,
- heads=12,
- output_dim=512,
- use_checkpoint=use_checkpoint,
- checkpoint_num=checkpoint_num,
- t_size=t_size,
- dw_reduction=dw_reduction,
- backbone_drop_path_rate=backbone_drop_path_rate,
- temporal_downsample=temporal_downsample,
- no_lmhra=no_lmhra,
- double_lmhra=double_lmhra,
- return_list=return_list,
- n_layers=n_layers,
- n_dim=n_dim,
- n_head=n_head,
- mlp_factor=mlp_factor,
- drop_path_rate=drop_path_rate,
- mlp_dropout=mlp_dropout,
- cls_dropout=cls_dropout,
- num_classes=num_classes,
- )
-
- if pretrained:
- print('load pretrained weights')
- state_dict = torch.load(_MODELS["ViT-B/16"], map_location='cpu')
- load_state_dict(model, state_dict)
- return model.eval()
-
-
-def uniformerv2_l14(
- pretrained=True, use_checkpoint=False, checkpoint_num=[0],
- t_size=16, dw_reduction=1.5, backbone_drop_path_rate=0.,
- temporal_downsample=True,
- no_lmhra=False, double_lmhra=True,
- return_list=[20, 21, 22, 23],
- n_layers=4, n_dim=1024, n_head=16, mlp_factor=4.0, drop_path_rate=0.,
- mlp_dropout=[0.5, 0.5, 0.5, 0.5],
- cls_dropout=0.5, num_classes=400,
-):
- model = VisionTransformer(
- input_resolution=224,
- patch_size=14,
- width=1024,
- layers=24,
- heads=16,
- output_dim=768,
- use_checkpoint=use_checkpoint,
- checkpoint_num=checkpoint_num,
- t_size=t_size,
- dw_reduction=dw_reduction,
- backbone_drop_path_rate=backbone_drop_path_rate,
- temporal_downsample=temporal_downsample,
- no_lmhra=no_lmhra,
- double_lmhra=double_lmhra,
- return_list=return_list,
- n_layers=n_layers,
- n_dim=n_dim,
- n_head=n_head,
- mlp_factor=mlp_factor,
- drop_path_rate=drop_path_rate,
- mlp_dropout=mlp_dropout,
- cls_dropout=cls_dropout,
- num_classes=num_classes,
- )
-
- if pretrained:
- print('load pretrained weights')
- state_dict = torch.load(_MODELS["ViT-L/14"], map_location='cpu')
- load_state_dict(model, state_dict)
- return model.eval()
-
-
-def uniformerv2_l14_336(
- pretrained=True, use_checkpoint=False, checkpoint_num=[0],
- t_size=16, dw_reduction=1.5, backbone_drop_path_rate=0.,
- no_temporal_downsample=True,
- no_lmhra=False, double_lmhra=True,
- return_list=[20, 21, 22, 23],
- n_layers=4, n_dim=1024, n_head=16, mlp_factor=4.0, drop_path_rate=0.,
- mlp_dropout=[0.5, 0.5, 0.5, 0.5],
- cls_dropout=0.5, num_classes=400,
-):
- model = VisionTransformer(
- input_resolution=336,
- patch_size=14,
- width=1024,
- layers=24,
- heads=16,
- output_dim=768,
- use_checkpoint=use_checkpoint,
- checkpoint_num=checkpoint_num,
- t_size=t_size,
- dw_reduction=dw_reduction,
- backbone_drop_path_rate=backbone_drop_path_rate,
- no_temporal_downsample=no_temporal_downsample,
- no_lmhra=no_lmhra,
- double_lmhra=double_lmhra,
- return_list=return_list,
- n_layers=n_layers,
- n_dim=n_dim,
- n_head=n_head,
- mlp_factor=mlp_factor,
- drop_path_rate=drop_path_rate,
- mlp_dropout=mlp_dropout,
- cls_dropout=cls_dropout,
- num_classes=num_classes,
- )
-
- if pretrained:
- print('load pretrained weights')
- state_dict = torch.load(_MODELS["ViT-L/14_336"], map_location='cpu')
- load_state_dict(model, state_dict)
- return model.eval()
-
-
-if __name__ == '__main__':
- import time
- from fvcore.nn import FlopCountAnalysis
- from fvcore.nn import flop_count_table
- import numpy as np
-
- seed = 4217
- np.random.seed(seed)
- torch.manual_seed(seed)
- torch.cuda.manual_seed(seed)
- torch.cuda.manual_seed_all(seed)
- num_frames = 16
-
- model = uniformerv2_l14(
- pretrained=False,
- t_size=num_frames, backbone_drop_path_rate=0., drop_path_rate=0.,
- dw_reduction=1.5,
- no_lmhra=False,
- temporal_downsample=True,
- return_list=[8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23],
- mlp_dropout=[0.5]*16,
- n_layers=16
- )
- print(model)
-
- flops = FlopCountAnalysis(model, torch.rand(1, 3, num_frames, 224, 224))
- s = time.time()
- print(flop_count_table(flops, max_depth=1))
- print(time.time()-s)
\ No newline at end of file
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/regnet/README.md b/spaces/Andy1621/uniformer_image_detection/configs/regnet/README.md
deleted file mode 100644
index 0ccd4077b0b00557d4bd214dcc855561a25ec144..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/regnet/README.md
+++ /dev/null
@@ -1,96 +0,0 @@
-# Designing Network Design Spaces
-
-## Introduction
-
-[BACKBONE]
-
-We implement RegNetX and RegNetY models in detection systems and provide their first results on Mask R-CNN, Faster R-CNN and RetinaNet.
-
-The pre-trained modles are converted from [model zoo of pycls](https://github.com/facebookresearch/pycls/blob/master/MODEL_ZOO.md).
-
-```latex
-@article{radosavovic2020designing,
- title={Designing Network Design Spaces},
- author={Ilija Radosavovic and Raj Prateek Kosaraju and Ross Girshick and Kaiming He and Piotr Dollár},
- year={2020},
- eprint={2003.13678},
- archivePrefix={arXiv},
- primaryClass={cs.CV}
-}
-```
-
-## Usage
-
-To use a regnet model, there are two steps to do:
-
-1. Convert the model to ResNet-style supported by MMDetection
-2. Modify backbone and neck in config accordingly
-
-### Convert model
-
-We already prepare models of FLOPs from 400M to 12G in our model zoo.
-
-For more general usage, we also provide script `regnet2mmdet.py` in the tools directory to convert the key of models pretrained by [pycls](https://github.com/facebookresearch/pycls/) to
-ResNet-style checkpoints used in MMDetection.
-
-```bash
-python -u tools/model_converters/regnet2mmdet.py ${PRETRAIN_PATH} ${STORE_PATH}
-```
-
-This script convert model from `PRETRAIN_PATH` and store the converted model in `STORE_PATH`.
-
-### Modify config
-
-The users can modify the config's `depth` of backbone and corresponding keys in `arch` according to the configs in the [pycls model zoo](https://github.com/facebookresearch/pycls/blob/master/MODEL_ZOO.md).
-The parameter `in_channels` in FPN can be found in the Figure 15 & 16 of the paper (`wi` in the legend).
-This directory already provides some configs with their performance, using RegNetX from 800MF to 12GF level.
-For other pre-trained models or self-implemented regnet models, the users are responsible to check these parameters by themselves.
-
-**Note**: Although Fig. 15 & 16 also provide `w0`, `wa`, `wm`, `group_w`, and `bot_mul` for `arch`, they are quantized thus inaccurate, using them sometimes produces different backbone that does not match the key in the pre-trained model.
-
-## Results
-
-### Mask R-CNN
-
-| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download |
-| :---------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: |
-| [R-50-FPN](../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py)| pytorch | 1x | 4.4 | 12.0 | 38.2 | 34.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205_050542.log.json) |
-|[RegNetX-3.2GF-FPN](./mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py)| pytorch | 1x |5.0 ||40.3|36.6|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_1x_coco_20200520_163141-2a9d1814.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_1x_coco_20200520_163141.log.json) |
-|[RegNetX-4.0GF-FPN](./mask_rcnn_regnetx-4GF_fpn_1x_coco.py)| pytorch | 1x |5.5||41.5|37.4|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco/mask_rcnn_regnetx-4GF_fpn_1x_coco_20200517_180217-32e9c92d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco/mask_rcnn_regnetx-4GF_fpn_1x_coco_20200517_180217.log.json) |
-| [R-101-FPN](../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py)| pytorch | 1x | 6.4 | 10.3 | 40.0 | 36.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204-1efe0ed5.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204_144809.log.json) |
-|[RegNetX-6.4GF-FPN](./mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py)| pytorch | 1x |6.1 ||41.0|37.1|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco/mask_rcnn_regnetx-6.4GF_fpn_1x_coco_20200517_180439-3a7aae83.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco/mask_rcnn_regnetx-6.4GF_fpn_1x_coco_20200517_180439.log.json) |
-| [X-101-32x4d-FPN](../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py) | pytorch | 1x | 7.6 | 9.4 | 41.9 | 37.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco/mask_rcnn_x101_32x4d_fpn_1x_coco_20200205-478d0b67.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco/mask_rcnn_x101_32x4d_fpn_1x_coco_20200205_034906.log.json) |
-|[RegNetX-8.0GF-FPN](./mask_rcnn_regnetx-8GF_fpn_1x_coco.py)| pytorch | 1x |6.4 ||41.7|37.5|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco/mask_rcnn_regnetx-8GF_fpn_1x_coco_20200517_180515-09daa87e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco/mask_rcnn_regnetx-8GF_fpn_1x_coco_20200517_180515.log.json) |
-|[RegNetX-12GF-FPN](./mask_rcnn_regnetx-12GF_fpn_1x_coco.py)| pytorch | 1x |7.4 ||42.2|38|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco/mask_rcnn_regnetx-12GF_fpn_1x_coco_20200517_180552-b538bd8b.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco/mask_rcnn_regnetx-12GF_fpn_1x_coco_20200517_180552.log.json) |
-|[RegNetX-3.2GF-FPN-DCN-C3-C5](./mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py)| pytorch | 1x |5.0 ||40.3|36.6|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco_20200520_172726-75f40794.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco_20200520_172726.log.json) |
-
-### Faster R-CNN
-
-| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download |
-| :---------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: |
-| [R-50-FPN](../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py)| pytorch | 1x | 4.0 | 18.2 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json) |
-|[RegNetX-3.2GF-FPN](./faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py)| pytorch | 1x | 4.5||39.9|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco/faster_rcnn_regnetx-3.2GF_fpn_1x_coco_20200517_175927-126fd9bf.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco/faster_rcnn_regnetx-3.2GF_fpn_1x_coco_20200517_175927.log.json) |
-|[RegNetX-3.2GF-FPN](./faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py)| pytorch | 2x | 4.5||41.1|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco/faster_rcnn_regnetx-3.2GF_fpn_2x_coco_20200520_223955-e2081918.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco/faster_rcnn_regnetx-3.2GF_fpn_2x_coco_20200520_223955.log.json) |
-
-### RetinaNet
-
-| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download |
-| :---------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: |
-| [R-50-FPN](../retinanet/retinanet_r50_fpn_1x_coco.py) | pytorch | 1x | 3.8 | 16.6 | 36.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130_002941.log.json) |
-|[RegNetX-800MF-FPN](./retinanet_regnetx-800MF_fpn_1x_coco.py)| pytorch | 1x |2.5||35.6|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-800MF_fpn_1x_coco/retinanet_regnetx-800MF_fpn_1x_coco_20200517_191403-f6f91d10.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-800MF_fpn_1x_coco/retinanet_regnetx-800MF_fpn_1x_coco_20200517_191403.log.json) |
-|[RegNetX-1.6GF-FPN](./retinanet_regnetx-1.6GF_fpn_1x_coco.py)| pytorch | 1x |3.3||37.3|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco/retinanet_regnetx-1.6GF_fpn_1x_coco_20200517_191403-37009a9d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco/retinanet_regnetx-1.6GF_fpn_1x_coco_20200517_191403.log.json) |
-|[RegNetX-3.2GF-FPN](./retinanet_regnetx-3.2GF_fpn_1x_coco.py)| pytorch | 1x |4.2 ||39.1|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco/retinanet_regnetx-3.2GF_fpn_1x_coco_20200520_163141-cb1509e8.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco/retinanet_regnetx-3.2GF_fpn_1x_coco_20200520_163141.log.json) |
-
-### Pre-trained models
-
-We also train some models with longer schedules and multi-scale training. The users could finetune them for downstream tasks.
-
-| Method | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download |
-| :-----: | :-----: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: |
-|Faster RCNN |[RegNetX-3.2GF-FPN](./faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py)| pytorch | 3x |5.0 ||42.2|-|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200520_224253-bf85ae3e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200520_224253.log.json) |
-|Mask RCNN |[RegNetX-3.2GF-FPN](./mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py)| pytorch | 3x |5.0 ||43.1|38.7|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200521_202221-99879813.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200521_202221.log.json) |
-
-### Notice
-
-1. The models are trained using a different weight decay, i.e., `weight_decay=5e-5` according to the setting in ImageNet training. This brings improvement of at least 0.7 AP absolute but does not improve the model using ResNet-50.
-2. RetinaNets using RegNets are trained with learning rate 0.02 with gradient clip. We find that using learning rate 0.02 could improve the results by at least 0.7 AP absolute and gradient clip is necessary to stabilize the training. However, this does not improve the performance of ResNet-50-FPN RetinaNet.
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/encnet/encnet_r101-d8_512x512_80k_ade20k.py b/spaces/Andy1621/uniformer_image_segmentation/configs/encnet/encnet_r101-d8_512x512_80k_ade20k.py
deleted file mode 100644
index 94151004ea88394373cf8f135b065d5056b11179..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/encnet/encnet_r101-d8_512x512_80k_ade20k.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './encnet_r50-d8_512x512_80k_ade20k.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/fcn/README.md b/spaces/Andy1621/uniformer_image_segmentation/configs/fcn/README.md
deleted file mode 100644
index 270781b48b99fe13628f3b3f81fb7457a9052bc9..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/fcn/README.md
+++ /dev/null
@@ -1,85 +0,0 @@
-# Fully Convolutional Networks for Semantic Segmentation
-
-## Introduction
-
-
-
-```latex
-@article{shelhamer2017fully,
- title={Fully convolutional networks for semantic segmentation},
- author={Shelhamer, Evan and Long, Jonathan and Darrell, Trevor},
- journal={IEEE transactions on pattern analysis and machine intelligence},
- volume={39},
- number={4},
- pages={640--651},
- year={2017},
- publisher={IEEE Trans Pattern Anal Mach Intell}
-}
-```
-
-## Results and models
-
-### Cityscapes
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
-| ------ | ---------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| FCN | R-50-D8 | 512x1024 | 40000 | 5.7 | 4.17 | 72.25 | 73.36 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r50-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_40k_cityscapes/fcn_r50-d8_512x1024_40k_cityscapes_20200604_192608-efe53f0d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_40k_cityscapes/fcn_r50-d8_512x1024_40k_cityscapes_20200604_192608.log.json) |
-| FCN | R-101-D8 | 512x1024 | 40000 | 9.2 | 2.66 | 75.45 | 76.58 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_40k_cityscapes/fcn_r101-d8_512x1024_40k_cityscapes_20200604_181852-a883d3a1.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_40k_cityscapes/fcn_r101-d8_512x1024_40k_cityscapes_20200604_181852.log.json) |
-| FCN | R-50-D8 | 769x769 | 40000 | 6.5 | 1.80 | 71.47 | 72.54 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r50-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_40k_cityscapes/fcn_r50-d8_769x769_40k_cityscapes_20200606_113104-977b5d02.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_40k_cityscapes/fcn_r50-d8_769x769_40k_cityscapes_20200606_113104.log.json) |
-| FCN | R-101-D8 | 769x769 | 40000 | 10.4 | 1.19 | 73.93 | 75.14 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_40k_cityscapes/fcn_r101-d8_769x769_40k_cityscapes_20200606_113208-7d4ab69c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_40k_cityscapes/fcn_r101-d8_769x769_40k_cityscapes_20200606_113208.log.json) |
-| FCN | R-18-D8 | 512x1024 | 80000 | 1.7 | 14.65 | 71.11 | 72.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r18-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_512x1024_80k_cityscapes/fcn_r18-d8_512x1024_80k_cityscapes_20201225_021327-6c50f8b4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_512x1024_80k_cityscapes/fcn_r18-d8_512x1024_80k_cityscapes-20201225_021327.log.json) |
-| FCN | R-50-D8 | 512x1024 | 80000 | - | | 73.61 | 74.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r50-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_80k_cityscapes/fcn_r50-d8_512x1024_80k_cityscapes_20200606_113019-03aa804d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_80k_cityscapes/fcn_r50-d8_512x1024_80k_cityscapes_20200606_113019.log.json) |
-| FCN | R-101-D8 | 512x1024 | 80000 | - | - | 75.13 | 75.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_80k_cityscapes/fcn_r101-d8_512x1024_80k_cityscapes_20200606_113038-3fb937eb.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_80k_cityscapes/fcn_r101-d8_512x1024_80k_cityscapes_20200606_113038.log.json) |
-| FCN | R-18-D8 | 769x769 | 80000 | 1.9 | 6.40 | 70.80 | 73.16 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r18-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_769x769_80k_cityscapes/fcn_r18-d8_769x769_80k_cityscapes_20201225_021451-9739d1b8.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_769x769_80k_cityscapes/fcn_r18-d8_769x769_80k_cityscapes-20201225_021451.log.json) |
-| FCN | R-50-D8 | 769x769 | 80000 | - | - | 72.64 | 73.32 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r50-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_80k_cityscapes/fcn_r50-d8_769x769_80k_cityscapes_20200606_195749-f5caeabc.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_80k_cityscapes/fcn_r50-d8_769x769_80k_cityscapes_20200606_195749.log.json) |
-| FCN | R-101-D8 | 769x769 | 80000 | - | - | 75.52 | 76.61 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_80k_cityscapes/fcn_r101-d8_769x769_80k_cityscapes_20200606_214354-45cbac68.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_80k_cityscapes/fcn_r101-d8_769x769_80k_cityscapes_20200606_214354.log.json) |
-| FCN | R-18b-D8 | 512x1024 | 80000 | 1.6 | 16.74 | 70.24 | 72.77 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r18b-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_512x1024_80k_cityscapes/fcn_r18b-d8_512x1024_80k_cityscapes_20201225_230143-92c0f445.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_512x1024_80k_cityscapes/fcn_r18b-d8_512x1024_80k_cityscapes-20201225_230143.log.json) |
-| FCN | R-50b-D8 | 512x1024 | 80000 | 5.6 | 4.20 | 75.65 | 77.59 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r50b-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_512x1024_80k_cityscapes/fcn_r50b-d8_512x1024_80k_cityscapes_20201225_094221-82957416.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_512x1024_80k_cityscapes/fcn_r50b-d8_512x1024_80k_cityscapes-20201225_094221.log.json) |
-| FCN | R-101b-D8 | 512x1024 | 80000 | 9.1 | 2.73 | 77.37 | 78.77 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101b-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_512x1024_80k_cityscapes/fcn_r101b-d8_512x1024_80k_cityscapes_20201226_160213-4543858f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_512x1024_80k_cityscapes/fcn_r101b-d8_512x1024_80k_cityscapes-20201226_160213.log.json) |
-| FCN | R-18b-D8 | 769x769 | 80000 | 1.7 | 6.70 | 69.66 | 72.07 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r18b-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_769x769_80k_cityscapes/fcn_r18b-d8_769x769_80k_cityscapes_20201226_004430-32d504e5.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_769x769_80k_cityscapes/fcn_r18b-d8_769x769_80k_cityscapes-20201226_004430.log.json) |
-| FCN | R-50b-D8 | 769x769 | 80000 | 6.3 | 1.82 | 73.83 | 76.60 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r50b-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_769x769_80k_cityscapes/fcn_r50b-d8_769x769_80k_cityscapes_20201225_094223-94552d38.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_769x769_80k_cityscapes/fcn_r50b-d8_769x769_80k_cityscapes-20201225_094223.log.json) |
-| FCN | R-101b-D8 | 769x769 | 80000 | 10.3 | 1.15 | 77.02 | 78.67 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101b-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_769x769_80k_cityscapes/fcn_r101b-d8_769x769_80k_cityscapes_20201226_170012-82be37e2.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_769x769_80k_cityscapes/fcn_r101b-d8_769x769_80k_cityscapes-20201226_170012.log.json) |
-| FCN-D6 | R-50-D16 | 512x1024 | 40000 | 3.4 | 10.22 | 77.06 | 78.85 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes-98d5d1bc.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes-20210305_130133.log.json) |
-| FCN-D6 | R-50-D16 | 512x1024 | 80000 | - | 10.35 | 77.27 | 78.88 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes-98d5d1bc.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes/fcn_d6_r50-d16_512x1024_80k_cityscapes-20210306_115604.log.json) |
-| FCN-D6 | R-50-D16 | 769x769 | 40000 | 3.7 | 4.17 | 76.82 | 78.22 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes/fcn_d6_r50-d16_769x769_40k_cityscapes-1aab18ed.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes/fcn_d6_r50-d16_769x769_40k_cityscapes-20210305_185744.log.json) |
-| FCN-D6 | R-50-D16 | 769x769 | 80000 | - | 4.15 | 77.04 | 78.40 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes/fcn_d6_r50-d16_769x769_80k_cityscapes-109d88eb.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes/fcn_d6_r50-d16_769x769_80k_cityscapes-20210305_200413.log.json) |
-| FCN-D6 | R-101-D16 | 512x1024 | 40000 | 4.5 | 8.04 | 77.36 | 79.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes/fcn_d6_r101-d16_512x1024_40k_cityscapes-9cf2b450.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes/fcn_d6_r101-d16_512x1024_40k_cityscapes-20210305_130337.log.json) |
-| FCN-D6 | R-101-D16 | 512x1024 | 80000 | - | 8.26 | 78.46 | 80.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes/fcn_d6_r101-d16_512x1024_80k_cityscapes-cb336445.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes/fcn_d6_r101-d16_512x1024_80k_cityscapes-20210308_102747.log.json) |
-| FCN-D6 | R-101-D16 | 769x769 | 40000 | 5.0 | 3.12 | 77.28 | 78.95 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes/fcn_d6_r101-d16_769x769_40k_cityscapes-60b114e9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes/fcn_d6_r101-d16_769x769_40k_cityscapes-20210308_102453.log.json) |
-| FCN-D6 | R-101-D16 | 769x769 | 80000 | - | 3.21 | 78.06 | 79.58 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes/fcn_d6_r101-d16_769x769_80k_cityscapes-e33adc4f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes/fcn_d6_r101-d16_769x769_80k_cityscapes-20210306_120016.log.json) |
-| FCN-D6 | R-50b-D16 | 512x1024 | 80000 | 3.2 | 10.16 | 76.99 | 79.03 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r50b_d16_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_512x1024_80k_cityscapes/fcn_d6_r50b_d16_512x1024_80k_cityscapes-6a0b62e9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_512x1024_80k_cityscapes/fcn_d6_r50b_d16_512x1024_80k_cityscapes-20210311_125550.log.json) |
-| FCN-D6 | R-50b-D16 | 769x769 | 80000 | 3.6 | 4.17 | 76.86 | 78.52 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r50b_d16_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_769x769_80k_cityscapes/fcn_d6_r50b_d16_769x769_80k_cityscapes-d665f231.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_769x769_80k_cityscapes/fcn_d6_r50b_d16_769x769_80k_cityscapes-20210311_131012.log.json) |
-| FCN-D6 | R-101b-D16 | 512x1024 | 80000 | 4.3 | 8.46 | 77.72 | 79.53 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101b_d16_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_512x1024_80k_cityscapes/fcn_d6_r101b_d16_512x1024_80k_cityscapes-3f2eb5b4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_512x1024_80k_cityscapes/fcn_d6_r101b_d16_512x1024_80k_cityscapes-20210311_144305.log.json) |
-| FCN-D6 | R-101b-D16 | 769x769 | 80000 | 4.8 | 3.32 | 77.34 | 78.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101b_d16_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_769x769_80k_cityscapes/fcn_d6_r101b_d16_769x769_80k_cityscapes-c4d8bfbc.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_769x769_80k_cityscapes/fcn_d6_r101b_d16_769x769_80k_cityscapes-20210311_154527.log.json) |
-
-### ADE20K
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| FCN | R-50-D8 | 512x512 | 80000 | 8.5 | 23.49 | 35.94 | 37.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r50-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_80k_ade20k/fcn_r50-d8_512x512_80k_ade20k_20200614_144016-f8ac5082.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_80k_ade20k/fcn_r50-d8_512x512_80k_ade20k_20200614_144016.log.json) |
-| FCN | R-101-D8 | 512x512 | 80000 | 12 | 14.78 | 39.61 | 40.83 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_80k_ade20k/fcn_r101-d8_512x512_80k_ade20k_20200615_014143-bc1809f7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_80k_ade20k/fcn_r101-d8_512x512_80k_ade20k_20200615_014143.log.json) |
-| FCN | R-50-D8 | 512x512 | 160000 | - | - | 36.10 | 38.08 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r50-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_160k_ade20k/fcn_r50-d8_512x512_160k_ade20k_20200615_100713-4edbc3b4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_160k_ade20k/fcn_r50-d8_512x512_160k_ade20k_20200615_100713.log.json) |
-| FCN | R-101-D8 | 512x512 | 160000 | - | - | 39.91 | 41.40 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_160k_ade20k/fcn_r101-d8_512x512_160k_ade20k_20200615_105816-fd192bd5.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_160k_ade20k/fcn_r101-d8_512x512_160k_ade20k_20200615_105816.log.json) |
-
-### Pascal VOC 2012 + Aug
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| FCN | R-50-D8 | 512x512 | 20000 | 5.7 | 23.28 | 67.08 | 69.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r50-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_20k_voc12aug/fcn_r50-d8_512x512_20k_voc12aug_20200617_010715-52dc5306.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_20k_voc12aug/fcn_r50-d8_512x512_20k_voc12aug_20200617_010715.log.json) |
-| FCN | R-101-D8 | 512x512 | 20000 | 9.2 | 14.81 | 71.16 | 73.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_20k_voc12aug/fcn_r101-d8_512x512_20k_voc12aug_20200617_010842-0bb4e798.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_20k_voc12aug/fcn_r101-d8_512x512_20k_voc12aug_20200617_010842.log.json) |
-| FCN | R-50-D8 | 512x512 | 40000 | - | - | 66.97 | 69.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r50-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_40k_voc12aug/fcn_r50-d8_512x512_40k_voc12aug_20200613_161222-5e2dbf40.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_40k_voc12aug/fcn_r50-d8_512x512_40k_voc12aug_20200613_161222.log.json) |
-| FCN | R-101-D8 | 512x512 | 40000 | - | - | 69.91 | 72.38 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_40k_voc12aug/fcn_r101-d8_512x512_40k_voc12aug_20200613_161240-4c8bcefd.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_40k_voc12aug/fcn_r101-d8_512x512_40k_voc12aug_20200613_161240.log.json) |
-
-### Pascal Context
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| FCN | R-101-D8 | 480x480 | 40000 | - | 9.93 | 44.43 | 45.63 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101-d8_480x480_40k_pascal_context.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context/fcn_r101-d8_480x480_40k_pascal_context-20210421_154757-b5e97937.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context/fcn_r101-d8_480x480_40k_pascal_context-20210421_154757.log.json) |
-| FCN | R-101-D8 | 480x480 | 80000 | - | - | 44.13 | 45.26 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101-d8_480x480_80k_pascal_context.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context/fcn_r101-d8_480x480_80k_pascal_context-20210421_163310-4711813f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context/fcn_r101-d8_480x480_80k_pascal_context-20210421_163310.log.json) |
-
-### Pascal Context 59
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| FCN | R-101-D8 | 480x480 | 40000 | - | - | 48.42 | 50.4 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101-d8_480x480_40k_pascal_context_59.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context_59/fcn_r101-d8_480x480_40k_pascal_context_59_20210415_230724-8cf83682.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context_59/fcn_r101-d8_480x480_40k_pascal_context_59-20210415_230724.log.json) |
-| FCN | R-101-D8 | 480x480 | 80000 | - | - | 49.35 | 51.38 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101-d8_480x480_80k_pascal_context_59.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context_59/fcn_r101-d8_480x480_80k_pascal_context_59_20210416_110804-9a6f2c94.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context_59/fcn_r101-d8_480x480_80k_pascal_context_59-20210416_110804.log.json) |
diff --git a/spaces/AnthonyTruchetPoC/persistent-docker/scripts/run-tests.sh b/spaces/AnthonyTruchetPoC/persistent-docker/scripts/run-tests.sh
deleted file mode 100644
index 52121b0317fd794d3ca23a26e1456060c7aca7e1..0000000000000000000000000000000000000000
--- a/spaces/AnthonyTruchetPoC/persistent-docker/scripts/run-tests.sh
+++ /dev/null
@@ -1,2 +0,0 @@
-#!/usr/bin/env sh
-poetry run pytest
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/docs/notes/benchmarks.md b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/docs/notes/benchmarks.md
deleted file mode 100644
index b41588daf3a039b9034e80366c2710e90ba3e056..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/docs/notes/benchmarks.md
+++ /dev/null
@@ -1,196 +0,0 @@
-
-# Benchmarks
-
-Here we benchmark the training speed of a Mask R-CNN in detectron2,
-with some other popular open source Mask R-CNN implementations.
-
-
-### Settings
-
-* Hardware: 8 NVIDIA V100s with NVLink.
-* Software: Python 3.7, CUDA 10.1, cuDNN 7.6.5, PyTorch 1.5,
- TensorFlow 1.15.0rc2, Keras 2.2.5, MxNet 1.6.0b20190820.
-* Model: an end-to-end R-50-FPN Mask-RCNN model, using the same hyperparameter as the
- [Detectron baseline config](https://github.com/facebookresearch/Detectron/blob/master/configs/12_2017_baselines/e2e_mask_rcnn_R-50-FPN_1x.yaml)
- (it does not have scale augmentation).
-* Metrics: We use the average throughput in iterations 100-500 to skip GPU warmup time.
- Note that for R-CNN-style models, the throughput of a model typically changes during training, because
- it depends on the predictions of the model. Therefore this metric is not directly comparable with
- "train speed" in model zoo, which is the average speed of the entire training run.
-
-
-### Main Results
-
-```eval_rst
-+-------------------------------+--------------------+
-| Implementation | Throughput (img/s) |
-+===============================+====================+
-| |D2| |PT| | 62 |
-+-------------------------------+--------------------+
-| mmdetection_ |PT| | 53 |
-+-------------------------------+--------------------+
-| maskrcnn-benchmark_ |PT| | 53 |
-+-------------------------------+--------------------+
-| tensorpack_ |TF| | 50 |
-+-------------------------------+--------------------+
-| simpledet_ |mxnet| | 39 |
-+-------------------------------+--------------------+
-| Detectron_ |C2| | 19 |
-+-------------------------------+--------------------+
-| `matterport/Mask_RCNN`__ |TF| | 14 |
-+-------------------------------+--------------------+
-
-.. _maskrcnn-benchmark: https://github.com/facebookresearch/maskrcnn-benchmark/
-.. _tensorpack: https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN
-.. _mmdetection: https://github.com/open-mmlab/mmdetection/
-.. _simpledet: https://github.com/TuSimple/simpledet/
-.. _Detectron: https://github.com/facebookresearch/Detectron
-__ https://github.com/matterport/Mask_RCNN/
-
-.. |D2| image:: https://github.com/facebookresearch/detectron2/raw/main/.github/Detectron2-Logo-Horz.svg?sanitize=true
- :height: 15pt
- :target: https://github.com/facebookresearch/detectron2/
-.. |PT| image:: https://pytorch.org/assets/images/logo-icon.svg
- :width: 15pt
- :height: 15pt
- :target: https://pytorch.org
-.. |TF| image:: https://static.nvidiagrid.net/ngc/containers/tensorflow.png
- :width: 15pt
- :height: 15pt
- :target: https://tensorflow.org
-.. |mxnet| image:: https://github.com/dmlc/web-data/raw/master/mxnet/image/mxnet_favicon.png
- :width: 15pt
- :height: 15pt
- :target: https://mxnet.apache.org/
-.. |C2| image:: https://caffe2.ai/static/logo.svg
- :width: 15pt
- :height: 15pt
- :target: https://caffe2.ai
-```
-
-
-Details for each implementation:
-
-* __Detectron2__: with release v0.1.2, run:
- ```
- python tools/train_net.py --config-file configs/Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x.yaml --num-gpus 8
- ```
-
-* __mmdetection__: at commit `b0d845f`, run
- ```
- ./tools/dist_train.sh configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py 8
- ```
-
-* __maskrcnn-benchmark__: use commit `0ce8f6f` with `sed -i 's/torch.uint8/torch.bool/g' **/*.py; sed -i 's/AT_CHECK/TORCH_CHECK/g' **/*.cu`
- to make it compatible with PyTorch 1.5. Then, run training with
- ```
- python -m torch.distributed.launch --nproc_per_node=8 tools/train_net.py --config-file configs/e2e_mask_rcnn_R_50_FPN_1x.yaml
- ```
- The speed we observed is faster than its model zoo, likely due to different software versions.
-
-* __tensorpack__: at commit `caafda`, `export TF_CUDNN_USE_AUTOTUNE=0`, then run
- ```
- mpirun -np 8 ./train.py --config DATA.BASEDIR=/data/coco TRAINER=horovod BACKBONE.STRIDE_1X1=True TRAIN.STEPS_PER_EPOCH=50 --load ImageNet-R50-AlignPadding.npz
- ```
-
-* __SimpleDet__: at commit `9187a1`, run
- ```
- python detection_train.py --config config/mask_r50v1_fpn_1x.py
- ```
-
-* __Detectron__: run
- ```
- python tools/train_net.py --cfg configs/12_2017_baselines/e2e_mask_rcnn_R-50-FPN_1x.yaml
- ```
- Note that many of its ops run on CPUs, therefore the performance is limited.
-
-* __matterport/Mask_RCNN__: at commit `3deaec`, apply the following diff, `export TF_CUDNN_USE_AUTOTUNE=0`, then run
- ```
- python coco.py train --dataset=/data/coco/ --model=imagenet
- ```
- Note that many small details in this implementation might be different
- from Detectron's standards.
-
-
-
- (diff to make it use the same hyperparameters - click to expand)
-
-
- ```diff
- diff --git i/mrcnn/model.py w/mrcnn/model.py
- index 62cb2b0..61d7779 100644
- --- i/mrcnn/model.py
- +++ w/mrcnn/model.py
- @@ -2367,8 +2367,8 @@ class MaskRCNN():
- epochs=epochs,
- steps_per_epoch=self.config.STEPS_PER_EPOCH,
- callbacks=callbacks,
- - validation_data=val_generator,
- - validation_steps=self.config.VALIDATION_STEPS,
- + #validation_data=val_generator,
- + #validation_steps=self.config.VALIDATION_STEPS,
- max_queue_size=100,
- workers=workers,
- use_multiprocessing=True,
- diff --git i/mrcnn/parallel_model.py w/mrcnn/parallel_model.py
- index d2bf53b..060172a 100644
- --- i/mrcnn/parallel_model.py
- +++ w/mrcnn/parallel_model.py
- @@ -32,6 +32,7 @@ class ParallelModel(KM.Model):
- keras_model: The Keras model to parallelize
- gpu_count: Number of GPUs. Must be > 1
- """
- + super().__init__()
- self.inner_model = keras_model
- self.gpu_count = gpu_count
- merged_outputs = self.make_parallel()
- diff --git i/samples/coco/coco.py w/samples/coco/coco.py
- index 5d172b5..239ed75 100644
- --- i/samples/coco/coco.py
- +++ w/samples/coco/coco.py
- @@ -81,7 +81,10 @@ class CocoConfig(Config):
- IMAGES_PER_GPU = 2
-
- # Uncomment to train on 8 GPUs (default is 1)
- - # GPU_COUNT = 8
- + GPU_COUNT = 8
- + BACKBONE = "resnet50"
- + STEPS_PER_EPOCH = 50
- + TRAIN_ROIS_PER_IMAGE = 512
-
- # Number of classes (including background)
- NUM_CLASSES = 1 + 80 # COCO has 80 classes
- @@ -496,29 +499,10 @@ if __name__ == '__main__':
- # *** This training schedule is an example. Update to your needs ***
-
- # Training - Stage 1
- - print("Training network heads")
- model.train(dataset_train, dataset_val,
- learning_rate=config.LEARNING_RATE,
- epochs=40,
- - layers='heads',
- - augmentation=augmentation)
- -
- - # Training - Stage 2
- - # Finetune layers from ResNet stage 4 and up
- - print("Fine tune Resnet stage 4 and up")
- - model.train(dataset_train, dataset_val,
- - learning_rate=config.LEARNING_RATE,
- - epochs=120,
- - layers='4+',
- - augmentation=augmentation)
- -
- - # Training - Stage 3
- - # Fine tune all layers
- - print("Fine tune all layers")
- - model.train(dataset_train, dataset_val,
- - learning_rate=config.LEARNING_RATE / 10,
- - epochs=160,
- - layers='all',
- + layers='3+',
- augmentation=augmentation)
-
- elif args.command == "evaluate":
- ```
-
-
diff --git a/spaces/Ayanoaisho/L/README.md b/spaces/Ayanoaisho/L/README.md
deleted file mode 100644
index 7e18b0fd1b94654b2528cfb48d7a74dafcde910d..0000000000000000000000000000000000000000
--- a/spaces/Ayanoaisho/L/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: L
-emoji: ⚡
-colorFrom: green
-colorTo: yellow
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/AzumaSeren100/XuanShen-Bert-VITS2/text/tone_sandhi.py b/spaces/AzumaSeren100/XuanShen-Bert-VITS2/text/tone_sandhi.py
deleted file mode 100644
index 0f45b7a72c5d858bcaab19ac85cfa686bf9a74da..0000000000000000000000000000000000000000
--- a/spaces/AzumaSeren100/XuanShen-Bert-VITS2/text/tone_sandhi.py
+++ /dev/null
@@ -1,351 +0,0 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from typing import List
-from typing import Tuple
-
-import jieba
-from pypinyin import lazy_pinyin
-from pypinyin import Style
-
-
-class ToneSandhi():
- def __init__(self):
- self.must_neural_tone_words = {
- '麻烦', '麻利', '鸳鸯', '高粱', '骨头', '骆驼', '马虎', '首饰', '馒头', '馄饨', '风筝',
- '难为', '队伍', '阔气', '闺女', '门道', '锄头', '铺盖', '铃铛', '铁匠', '钥匙', '里脊',
- '里头', '部分', '那么', '道士', '造化', '迷糊', '连累', '这么', '这个', '运气', '过去',
- '软和', '转悠', '踏实', '跳蚤', '跟头', '趔趄', '财主', '豆腐', '讲究', '记性', '记号',
- '认识', '规矩', '见识', '裁缝', '补丁', '衣裳', '衣服', '衙门', '街坊', '行李', '行当',
- '蛤蟆', '蘑菇', '薄荷', '葫芦', '葡萄', '萝卜', '荸荠', '苗条', '苗头', '苍蝇', '芝麻',
- '舒服', '舒坦', '舌头', '自在', '膏药', '脾气', '脑袋', '脊梁', '能耐', '胳膊', '胭脂',
- '胡萝', '胡琴', '胡同', '聪明', '耽误', '耽搁', '耷拉', '耳朵', '老爷', '老实', '老婆',
- '老头', '老太', '翻腾', '罗嗦', '罐头', '编辑', '结实', '红火', '累赘', '糨糊', '糊涂',
- '精神', '粮食', '簸箕', '篱笆', '算计', '算盘', '答应', '笤帚', '笑语', '笑话', '窟窿',
- '窝囊', '窗户', '稳当', '稀罕', '称呼', '秧歌', '秀气', '秀才', '福气', '祖宗', '砚台',
- '码头', '石榴', '石头', '石匠', '知识', '眼睛', '眯缝', '眨巴', '眉毛', '相声', '盘算',
- '白净', '痢疾', '痛快', '疟疾', '疙瘩', '疏忽', '畜生', '生意', '甘蔗', '琵琶', '琢磨',
- '琉璃', '玻璃', '玫瑰', '玄乎', '狐狸', '状元', '特务', '牲口', '牙碜', '牌楼', '爽快',
- '爱人', '热闹', '烧饼', '烟筒', '烂糊', '点心', '炊帚', '灯笼', '火候', '漂亮', '滑溜',
- '溜达', '温和', '清楚', '消息', '浪头', '活泼', '比方', '正经', '欺负', '模糊', '槟榔',
- '棺材', '棒槌', '棉花', '核桃', '栅栏', '柴火', '架势', '枕头', '枇杷', '机灵', '本事',
- '木头', '木匠', '朋友', '月饼', '月亮', '暖和', '明白', '时候', '新鲜', '故事', '收拾',
- '收成', '提防', '挖苦', '挑剔', '指甲', '指头', '拾掇', '拳头', '拨弄', '招牌', '招呼',
- '抬举', '护士', '折腾', '扫帚', '打量', '打算', '打点', '打扮', '打听', '打发', '扎实',
- '扁担', '戒指', '懒得', '意识', '意思', '情形', '悟性', '怪物', '思量', '怎么', '念头',
- '念叨', '快活', '忙活', '志气', '心思', '得罪', '张罗', '弟兄', '开通', '应酬', '庄稼',
- '干事', '帮手', '帐篷', '希罕', '师父', '师傅', '巴结', '巴掌', '差事', '工夫', '岁数',
- '屁股', '尾巴', '少爷', '小气', '小伙', '将就', '对头', '对付', '寡妇', '家伙', '客气',
- '实在', '官司', '学问', '学生', '字号', '嫁妆', '媳妇', '媒人', '婆家', '娘家', '委屈',
- '姑娘', '姐夫', '妯娌', '妥当', '妖精', '奴才', '女婿', '头发', '太阳', '大爷', '大方',
- '大意', '大夫', '多少', '多么', '外甥', '壮实', '地道', '地方', '在乎', '困难', '嘴巴',
- '嘱咐', '嘟囔', '嘀咕', '喜欢', '喇嘛', '喇叭', '商量', '唾沫', '哑巴', '哈欠', '哆嗦',
- '咳嗽', '和尚', '告诉', '告示', '含糊', '吓唬', '后头', '名字', '名堂', '合同', '吆喝',
- '叫唤', '口袋', '厚道', '厉害', '千斤', '包袱', '包涵', '匀称', '勤快', '动静', '动弹',
- '功夫', '力气', '前头', '刺猬', '刺激', '别扭', '利落', '利索', '利害', '分析', '出息',
- '凑合', '凉快', '冷战', '冤枉', '冒失', '养活', '关系', '先生', '兄弟', '便宜', '使唤',
- '佩服', '作坊', '体面', '位置', '似的', '伙计', '休息', '什么', '人家', '亲戚', '亲家',
- '交情', '云彩', '事情', '买卖', '主意', '丫头', '丧气', '两口', '东西', '东家', '世故',
- '不由', '不在', '下水', '下巴', '上头', '上司', '丈夫', '丈人', '一辈', '那个', '菩萨',
- '父亲', '母亲', '咕噜', '邋遢', '费用', '冤家', '甜头', '介绍', '荒唐', '大人', '泥鳅',
- '幸福', '熟悉', '计划', '扑腾', '蜡烛', '姥爷', '照顾', '喉咙', '吉他', '弄堂', '蚂蚱',
- '凤凰', '拖沓', '寒碜', '糟蹋', '倒腾', '报复', '逻辑', '盘缠', '喽啰', '牢骚', '咖喱',
- '扫把', '惦记'
- }
- self.must_not_neural_tone_words = {
- "男子", "女子", "分子", "原子", "量子", "莲子", "石子", "瓜子", "电子", "人人", "虎虎"
- }
- self.punc = "：，；。？！“”‘’':,;.?!"
-
- # the meaning of jieba pos tag: https://blog.csdn.net/weixin_44174352/article/details/113731041
- # e.g.
- # word: "家里"
- # pos: "s"
- # finals: ['ia1', 'i3']
- def _neural_sandhi(self, word: str, pos: str,
- finals: List[str]) -> List[str]:
-
- # reduplication words for n. and v. e.g. 奶奶, 试试, 旺旺
- for j, item in enumerate(word):
- if j - 1 >= 0 and item == word[j - 1] and pos[0] in {
- "n", "v", "a"
- } and word not in self.must_not_neural_tone_words:
- finals[j] = finals[j][:-1] + "5"
- ge_idx = word.find("个")
- if len(word) >= 1 and word[-1] in "吧呢啊呐噻嘛吖嗨呐哦哒额滴哩哟喽啰耶喔诶":
- finals[-1] = finals[-1][:-1] + "5"
- elif len(word) >= 1 and word[-1] in "的地得":
- finals[-1] = finals[-1][:-1] + "5"
- # e.g. 走了, 看着, 去过
- # elif len(word) == 1 and word in "了着过" and pos in {"ul", "uz", "ug"}:
- # finals[-1] = finals[-1][:-1] + "5"
- elif len(word) > 1 and word[-1] in "们子" and pos in {
- "r", "n"
- } and word not in self.must_not_neural_tone_words:
- finals[-1] = finals[-1][:-1] + "5"
- # e.g. 桌上, 地下, 家里
- elif len(word) > 1 and word[-1] in "上下里" and pos in {"s", "l", "f"}:
- finals[-1] = finals[-1][:-1] + "5"
- # e.g. 上来, 下去
- elif len(word) > 1 and word[-1] in "来去" and word[-2] in "上下进出回过起开":
- finals[-1] = finals[-1][:-1] + "5"
- # 个做量词
- elif (ge_idx >= 1 and
- (word[ge_idx - 1].isnumeric() or
- word[ge_idx - 1] in "几有两半多各整每做是")) or word == '个':
- finals[ge_idx] = finals[ge_idx][:-1] + "5"
- else:
- if word in self.must_neural_tone_words or word[
- -2:] in self.must_neural_tone_words:
- finals[-1] = finals[-1][:-1] + "5"
-
- word_list = self._split_word(word)
- finals_list = [finals[:len(word_list[0])], finals[len(word_list[0]):]]
- for i, word in enumerate(word_list):
- # conventional neural in Chinese
- if word in self.must_neural_tone_words or word[
- -2:] in self.must_neural_tone_words:
- finals_list[i][-1] = finals_list[i][-1][:-1] + "5"
- finals = sum(finals_list, [])
- return finals
-
- def _bu_sandhi(self, word: str, finals: List[str]) -> List[str]:
- # e.g. 看不懂
- if len(word) == 3 and word[1] == "不":
- finals[1] = finals[1][:-1] + "5"
- else:
- for i, char in enumerate(word):
- # "不" before tone4 should be bu2, e.g. 不怕
- if char == "不" and i + 1 < len(word) and finals[i +
- 1][-1] == "4":
- finals[i] = finals[i][:-1] + "2"
- return finals
-
- def _yi_sandhi(self, word: str, finals: List[str]) -> List[str]:
- # "一" in number sequences, e.g. 一零零, 二一零
- if word.find("一") != -1 and all(
- [item.isnumeric() for item in word if item != "一"]):
- return finals
- # "一" between reduplication words shold be yi5, e.g. 看一看
- elif len(word) == 3 and word[1] == "一" and word[0] == word[-1]:
- finals[1] = finals[1][:-1] + "5"
- # when "一" is ordinal word, it should be yi1
- elif word.startswith("第一"):
- finals[1] = finals[1][:-1] + "1"
- else:
- for i, char in enumerate(word):
- if char == "一" and i + 1 < len(word):
- # "一" before tone4 should be yi2, e.g. 一段
- if finals[i + 1][-1] == "4":
- finals[i] = finals[i][:-1] + "2"
- # "一" before non-tone4 should be yi4, e.g. 一天
- else:
- # "一" 后面如果是标点，还读一声
- if word[i + 1] not in self.punc:
- finals[i] = finals[i][:-1] + "4"
- return finals
-
- def _split_word(self, word: str) -> List[str]:
- word_list = jieba.cut_for_search(word)
- word_list = sorted(word_list, key=lambda i: len(i), reverse=False)
- first_subword = word_list[0]
- first_begin_idx = word.find(first_subword)
- if first_begin_idx == 0:
- second_subword = word[len(first_subword):]
- new_word_list = [first_subword, second_subword]
- else:
- second_subword = word[:-len(first_subword)]
- new_word_list = [second_subword, first_subword]
- return new_word_list
-
- def _three_sandhi(self, word: str, finals: List[str]) -> List[str]:
- if len(word) == 2 and self._all_tone_three(finals):
- finals[0] = finals[0][:-1] + "2"
- elif len(word) == 3:
- word_list = self._split_word(word)
- if self._all_tone_three(finals):
- # disyllabic + monosyllabic, e.g. 蒙古/包
- if len(word_list[0]) == 2:
- finals[0] = finals[0][:-1] + "2"
- finals[1] = finals[1][:-1] + "2"
- # monosyllabic + disyllabic, e.g. 纸/老虎
- elif len(word_list[0]) == 1:
- finals[1] = finals[1][:-1] + "2"
- else:
- finals_list = [
- finals[:len(word_list[0])], finals[len(word_list[0]):]
- ]
- if len(finals_list) == 2:
- for i, sub in enumerate(finals_list):
- # e.g. 所有/人
- if self._all_tone_three(sub) and len(sub) == 2:
- finals_list[i][0] = finals_list[i][0][:-1] + "2"
- # e.g. 好/喜欢
- elif i == 1 and not self._all_tone_three(sub) and finals_list[i][0][-1] == "3" and \
- finals_list[0][-1][-1] == "3":
-
- finals_list[0][-1] = finals_list[0][-1][:-1] + "2"
- finals = sum(finals_list, [])
- # split idiom into two words who's length is 2
- elif len(word) == 4:
- finals_list = [finals[:2], finals[2:]]
- finals = []
- for sub in finals_list:
- if self._all_tone_three(sub):
- sub[0] = sub[0][:-1] + "2"
- finals += sub
-
- return finals
-
- def _all_tone_three(self, finals: List[str]) -> bool:
- return all(x[-1] == "3" for x in finals)
-
- # merge "不" and the word behind it
- # if don't merge, "不" sometimes appears alone according to jieba, which may occur sandhi error
- def _merge_bu(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
- new_seg = []
- last_word = ""
- for word, pos in seg:
- if last_word == "不":
- word = last_word + word
- if word != "不":
- new_seg.append((word, pos))
- last_word = word[:]
- if last_word == "不":
- new_seg.append((last_word, 'd'))
- last_word = ""
- return new_seg
-
- # function 1: merge "一" and reduplication words in it's left and right, e.g. "听","一","听" ->"听一听"
- # function 2: merge single "一" and the word behind it
- # if don't merge, "一" sometimes appears alone according to jieba, which may occur sandhi error
- # e.g.
- # input seg: [('听', 'v'), ('一', 'm'), ('听', 'v')]
- # output seg: [['听一听', 'v']]
- def _merge_yi(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
- new_seg = []
- # function 1
- for i, (word, pos) in enumerate(seg):
- if i - 1 >= 0 and word == "一" and i + 1 < len(seg) and seg[i - 1][
- 0] == seg[i + 1][0] and seg[i - 1][1] == "v":
- new_seg[i - 1][0] = new_seg[i - 1][0] + "一" + new_seg[i - 1][0]
- else:
- if i - 2 >= 0 and seg[i - 1][0] == "一" and seg[i - 2][
- 0] == word and pos == "v":
- continue
- else:
- new_seg.append([word, pos])
- seg = new_seg
- new_seg = []
- # function 2
- for i, (word, pos) in enumerate(seg):
- if new_seg and new_seg[-1][0] == "一":
- new_seg[-1][0] = new_seg[-1][0] + word
- else:
- new_seg.append([word, pos])
- return new_seg
-
- # the first and the second words are all_tone_three
- def _merge_continuous_three_tones(
- self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
- new_seg = []
- sub_finals_list = [
- lazy_pinyin(
- word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
- for (word, pos) in seg
- ]
- assert len(sub_finals_list) == len(seg)
- merge_last = [False] * len(seg)
- for i, (word, pos) in enumerate(seg):
- if i - 1 >= 0 and self._all_tone_three(
- sub_finals_list[i - 1]) and self._all_tone_three(
- sub_finals_list[i]) and not merge_last[i - 1]:
- # if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi
- if not self._is_reduplication(seg[i - 1][0]) and len(
- seg[i - 1][0]) + len(seg[i][0]) <= 3:
- new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
- merge_last[i] = True
- else:
- new_seg.append([word, pos])
- else:
- new_seg.append([word, pos])
-
- return new_seg
-
- def _is_reduplication(self, word: str) -> bool:
- return len(word) == 2 and word[0] == word[1]
-
- # the last char of first word and the first char of second word is tone_three
- def _merge_continuous_three_tones_2(
- self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
- new_seg = []
- sub_finals_list = [
- lazy_pinyin(
- word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
- for (word, pos) in seg
- ]
- assert len(sub_finals_list) == len(seg)
- merge_last = [False] * len(seg)
- for i, (word, pos) in enumerate(seg):
- if i - 1 >= 0 and sub_finals_list[i - 1][-1][-1] == "3" and sub_finals_list[i][0][-1] == "3" and not \
- merge_last[i - 1]:
- # if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi
- if not self._is_reduplication(seg[i - 1][0]) and len(
- seg[i - 1][0]) + len(seg[i][0]) <= 3:
- new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
- merge_last[i] = True
- else:
- new_seg.append([word, pos])
- else:
- new_seg.append([word, pos])
- return new_seg
-
- def _merge_er(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
- new_seg = []
- for i, (word, pos) in enumerate(seg):
- if i - 1 >= 0 and word == "儿" and seg[i-1][0] != "#":
- new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
- else:
- new_seg.append([word, pos])
- return new_seg
-
- def _merge_reduplication(
- self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
- new_seg = []
- for i, (word, pos) in enumerate(seg):
- if new_seg and word == new_seg[-1][0]:
- new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
- else:
- new_seg.append([word, pos])
- return new_seg
-
- def pre_merge_for_modify(
- self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
- seg = self._merge_bu(seg)
- try:
- seg = self._merge_yi(seg)
- except:
- print("_merge_yi failed")
- seg = self._merge_reduplication(seg)
- seg = self._merge_continuous_three_tones(seg)
- seg = self._merge_continuous_three_tones_2(seg)
- seg = self._merge_er(seg)
- return seg
-
- def modified_tone(self, word: str, pos: str,
- finals: List[str]) -> List[str]:
- finals = self._bu_sandhi(word, finals)
- finals = self._yi_sandhi(word, finals)
- finals = self._neural_sandhi(word, pos, finals)
- finals = self._three_sandhi(word, finals)
- return finals
diff --git a/spaces/Benson/text-generation/Examples/Carx Deriva Carreras Mod Apk Vieja Versin.md b/spaces/Benson/text-generation/Examples/Carx Deriva Carreras Mod Apk Vieja Versin.md
deleted file mode 100644
index 29f23ed10aa6900ca080c663af825ec307e92425..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Carx Deriva Carreras Mod Apk Vieja Versin.md
+++ /dev/null
@@ -1,88 +0,0 @@
-
-
CarX Drift Racing Mod APK versión antigua: Una revisión
-
Si usted es un fan de los juegos de carreras de coches, es posible que haya oído hablar de CarX Drift Racing. Es uno de los juegos de deriva más populares y realistas en Android. Puede elegir entre una variedad de coches, personalizarlos y competir en diferentes pistas. También puede competir con otros jugadores en línea y mostrar sus habilidades a la deriva.
Pero ¿y si quieres disfrutar del juego sin limitaciones? ¿Qué pasa si quieres tener dinero y oro ilimitados, desbloquear todos los coches y pistas, y experimentar los mejores gráficos y la física? Bueno, usted puede hacer eso mediante la descarga de la versión apk mod de CarX Drift Racing. Y no solo cualquier apk mod, pero la versión anterior de la misma.
-
¿Por qué la versión anterior, se pregunta? Bueno, hay algunas razones por las que podría preferir la versión anterior a la última. En este artículo, vamos a revisar el CarX Drift Racing mod apk versión antigua, y le dirá cómo descargar e instalar en su dispositivo. También vamos a enumerar algunos pros y contras de usar este apk mod, y responder a algunas preguntas frecuentes.
-
¿Qué es CarX Drift Racing?
-
CarX Drift Racing es un juego de carreras desarrollado por CarX Technologies. Fue lanzado en 2014 para dispositivos Android e iOS. El juego se centra en la deriva, que es una técnica de conducción donde el conductor sobreviraje intencionalmente el coche para que se deslice hacia los lados. El juego cuenta con física realista, gráficos impresionantes y controles suaves. Puedes sentir la emoción de la deriva en varios modos, como el modo carrera, el modo en línea, el modo de ataque en el tiempo y el modo de entrenamiento.
-
-
-
Características de CarX Drift Racing
-
Algunas de las características principales de CarX Drift Racing son:
-
-
Física realista que simula el comportamiento de los coches reales en diferentes superficies
-
Impresionantes gráficos que crean una atmósfera inmersiva
-
Controles suaves que te permiten desplazarte fácilmente
-
Una variedad de coches para elegir, cada uno con diferentes características y sonidos
-
Muchas opciones de personalización para tus coches
-
Una variedad de pistas para competir, cada una con diferentes desafíos y entornos
-
Un modo de carrera donde se puede progresar a través de los niveles y ganar dinero y oro
-
Un modo online donde puedes competir con otros jugadores alrededor del mundo
-
Un modo de ataque de tiempo donde puedes establecer tus mejores tiempos de vuelta
-
Un modo de entrenamiento donde puedes practicar tus habilidades de deriva
-
Una tabla de clasificación donde puedes ver tu ranking entre otros jugadores
-
Un modo fantasma donde puedes competir contra tus propios fantasmas o los de otros jugadores
-
Un modo de repetición donde puedes ver las carreras de tus otros jugadores
-
Un modo de ajuste donde se puede ajustar la configuración de su coche para adaptarse a sus preferencias
-
Un garaje donde puedes guardar tus coches y ver sus estadísticas
-
-
¿Por qué descargar la versión antigua de CarX Drift Racing mod apk?
-
CarX Drift Racing es un gran juego, pero también tiene algunas limitaciones. Por ejemplo, necesitas gastar mucho dinero y oro para desbloquear y mejorar tus coches y pistas. También necesitas ver anuncios para obtener recompensas o bonificaciones. Y es posible que tengas problemas de compatibilidad o errores con la última versión del juego.
-
Es por eso que algunos jugadores prefieren descargar la versión anterior de CarX Drift Racing mod apk. Un apk mod es una versión modificada del juego original que tiene algunas características o cambios que no están disponibles en la versión oficial. Por ejemplo, la versión anterior de CarX Drift Racing mod apk ha:
-
-
-
Coches y pistas desbloqueados, para que pueda acceder a todo el contenido sin restricciones
-
Física realista y gráficos, para que pueda disfrutar de la mejor experiencia de juego
-
-
Al descargar la versión anterior de CarX Drift Racing mod apk, usted puede tener más diversión y libertad en el juego. También puede evitar algunos de los problemas que podrían ocurrir con la última versión del juego.
-
¿Cómo descargar e instalar CarX Drift Racing mod apk versión antigua?
-
Si desea descargar e instalar la versión antigua de CarX Drift Racing mod apk en su dispositivo, debe seguir estos pasos:
-
Paso 1: Descargar el archivo apk
-
El primer paso es descargar el archivo apk de CarX Drift Racing mod apk versión antigua. Puede encontrarlo en varios sitios web que ofrecen juegos y aplicaciones modificadas. Sin embargo, debe tener cuidado y elegir una fuente confiable y segura. Algunos sitios web pueden tener archivos falsos o maliciosos que pueden dañar su dispositivo o robar sus datos.
-
Uno de los sitios web que recomendamos es [APKPure]. Es un sitio web confiable y popular que proporciona archivos apk libres y puros para los usuarios de Android. Puede descargar CarX Drift Racing mod apk versión antigua de este [enlace]. El tamaño del archivo es de unos 300 MB, así que asegúrate de tener suficiente espacio en tu dispositivo.
-
Paso 2: Habilitar fuentes desconocidas
-
El siguiente paso es habilitar fuentes desconocidas en su dispositivo. Esta es una configuración de seguridad que le permite instalar aplicaciones desde fuentes distintas de Google Play Store. Para habilitar fuentes desconocidas, debe ir a la configuración del dispositivo, luego a la seguridad y luego a fuentes desconocidas. Pulse en el interruptor o en la casilla de verificación para activarlo.
-
Paso 3: Instalar el archivo apk
-
El tercer paso es instalar el archivo apk de CarX Drift Racing mod apk versión antigua. Para ello, debe localizar el archivo en el administrador de archivos o la carpeta de descargas de su dispositivo. Toque en el archivo y siga las instrucciones en la pantalla. La instalación puede tardar unos minutos.
-
-
El paso final es lanzar el juego y disfrutarlo. Puede encontrar el icono del juego en la pantalla de inicio del dispositivo o en el cajón de la aplicación. Tócalo y empieza a jugar. Verás que tienes dinero y oro ilimitados, coches y pistas desbloqueados, y física y gráficos realistas.
-
Pros y contras de CarX Drift Racing mod apk versión antigua
-
Como con cualquier juego modificado, hay algunos pros y contras de usar CarX Drift Racing mod apk versión antigua. Aquí están algunos de ellos:
-
Pros
-
-
Dinero y oro ilimitados
-
Esta es una de las principales ventajas de usar CarX Drift Racing mod apk versión antigua. Tendrás dinero y oro ilimitados en el juego, lo que significa que puedes comprar y actualizar lo que quieras. También puedes saltarte anuncios y obtener recompensas o bonos gratis.
-
Coches y pistas desbloqueados
-
Este es otro beneficio de usar CarX Drift Racing mod apk versión antigua. Usted tendrá acceso a todos los coches y pistas en el juego, sin restricciones. Puedes elegir entre más de 40 coches y más de 30 pistas, cada una con diferentes características y desafíos.
-
Física y gráficos realistas
-
Este es también un punto a favor de la utilización de CarX Drift Racing mod apk versión antigua. Disfrutará de la mejor experiencia de juego con física y gráficos realistas. Usted sentirá la emoción de la deriva en diferentes superficies, con impresionantes efectos y sonidos.
-
-
Contras
-
-
No es compatible con algunos dispositivos
-
Este es uno de los principales inconvenientes de usar CarX Drift Racing mod apk versión antigua. Es posible que tenga algunos problemas de compatibilidad con algunos dispositivos, especialmente los más nuevos. Es posible que el juego no funcione sin problemas o que se bloquee en algunos dispositivos, o que no pueda instalarlo en absoluto.
-
Puede contener errores y problemas técnicos
-
-
No actualizado con nuevas características y contenido
-
Este es también un punto menos de usar CarX Drift Racing mod apk versión antigua. Te perderás las nuevas características y contenido que ofrece la versión oficial del juego. Por ejemplo, no podrás jugar a los nuevos modos, coches, pistas, eventos o desafíos que los desarrolladores añaden al juego regularmente.
-
-
Conclusión
-
CarX Drift Racing es un juego de deriva divertido y realista que puedes jugar en tu dispositivo Android. Sin embargo, si quieres tener más libertad y diversión en el juego, se puede descargar la antigua versión de CarX Drift Racing mod apk. Este apk mod le dará dinero ilimitado y oro, coches desbloqueados y pistas, y la física realista y gráficos. Sin embargo, también debe ser consciente de los contras de usar este apk mod, tales como problemas de compatibilidad, errores y fallas, y la falta de actualizaciones.
-
Si está interesado en descargar e instalar CarX Drift Racing mod apk versión antigua, puede seguir los pasos que proporcionamos en este artículo. También recomendamos que utilice una fuente confiable y segura para descargar el archivo apk, como APKPure. Esperamos que este artículo sea útil e informativo para usted. ¡Feliz deriva!
-
Preguntas frecuentes
-
Aquí hay algunas preguntas frecuentes sobre CarX Drift Racing mod apk versión antigua:
-
-
Es CarX Drift Racing mod apk vieja versión segura de usar?
-
Sí, CarX Drift Racing mod apk versión antigua es seguro de usar, siempre y cuando se descarga desde una fuente confiable y segura. Sin embargo, siempre debe tener cuidado al descargar e instalar cualquier juego o aplicación modificada en su dispositivo, ya que algunos de ellos pueden contener virus o malware que pueden dañar su dispositivo o robar sus datos.
-
¿Puedo jugar CarX Drift Racing mod apk versión antigua en línea?
-
-
¿Puedo actualizar la versión antigua de CarX Drift Racing mod apk?
-
No, no se puede actualizar CarX Drift Racing mod apk versión antigua a la última versión del juego. Si intenta actualizarlo, perderá todas las características modificadas y los beneficios que tiene en la versión anterior. Por lo tanto, le aconsejamos que no lo actualice y disfrute de la versión anterior tal como está.
-
¿Puedo usar CarX Drift Racing mod apk versión antigua con mi cuenta existente?
-
No, no se puede utilizar CarX Drift Racing mod apk versión antigua con su cuenta existente. Si intentas hacerlo, puedes arriesgarte a perder tu cuenta o a que los desarrolladores del juego te prohíban acceder a ella. Por lo tanto, le recomendamos que cree una nueva cuenta o utilice una cuenta de invitado para jugar con este apk mod.
-
¿Puedo desinstalar CarX Drift Racing mod apk versión antigua?
-
Sí, puede desinstalar CarX Drift Racing mod apk versión antigua en cualquier momento que desee. Para ello, es necesario ir a la configuración de su dispositivo, a continuación, aplicaciones, a continuación, CarX Drift Racing. Toque en desinstalar y confirmar su elección. También puede eliminar el archivo apk del almacenamiento de su dispositivo si lo desea.
-
64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/dateutil/easter.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/dateutil/easter.py
deleted file mode 100644
index f74d1f7442473997245ac683b8a269a3574d1ba4..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/dateutil/easter.py
+++ /dev/null
@@ -1,89 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-This module offers a generic Easter computing method for any given year, using
-Western, Orthodox or Julian algorithms.
-"""
-
-import datetime
-
-__all__ = ["easter", "EASTER_JULIAN", "EASTER_ORTHODOX", "EASTER_WESTERN"]
-
-EASTER_JULIAN = 1
-EASTER_ORTHODOX = 2
-EASTER_WESTERN = 3
-
-
-def easter(year, method=EASTER_WESTERN):
- """
- This method was ported from the work done by GM Arts,
- on top of the algorithm by Claus Tondering, which was
- based in part on the algorithm of Ouding (1940), as
- quoted in "Explanatory Supplement to the Astronomical
- Almanac", P. Kenneth Seidelmann, editor.
-
- This algorithm implements three different Easter
- calculation methods:
-
- 1. Original calculation in Julian calendar, valid in
- dates after 326 AD
- 2. Original method, with date converted to Gregorian
- calendar, valid in years 1583 to 4099
- 3. Revised method, in Gregorian calendar, valid in
- years 1583 to 4099 as well
-
- These methods are represented by the constants:
-
- * ``EASTER_JULIAN = 1``
- * ``EASTER_ORTHODOX = 2``
- * ``EASTER_WESTERN = 3``
-
- The default method is method 3.
-
- More about the algorithm may be found at:
-
- `GM Arts: Easter Algorithms `_
-
- and
-
- `The Calendar FAQ: Easter `_
-
- """
-
- if not (1 <= method <= 3):
- raise ValueError("invalid method")
-
- # g - Golden year - 1
- # c - Century
- # h - (23 - Epact) mod 30
- # i - Number of days from March 21 to Paschal Full Moon
- # j - Weekday for PFM (0=Sunday, etc)
- # p - Number of days from March 21 to Sunday on or before PFM
- # (-6 to 28 methods 1 & 3, to 56 for method 2)
- # e - Extra days to add for method 2 (converting Julian
- # date to Gregorian date)
-
- y = year
- g = y % 19
- e = 0
- if method < 3:
- # Old method
- i = (19*g + 15) % 30
- j = (y + y//4 + i) % 7
- if method == 2:
- # Extra dates to convert Julian to Gregorian date
- e = 10
- if y > 1600:
- e = e + y//100 - 16 - (y//100 - 16)//4
- else:
- # New method
- c = y//100
- h = (c - c//4 - (8*c + 13)//25 + 19*g + 15) % 30
- i = h - (h//28)*(1 - (h//28)*(29//(h + 1))*((21 - g)//11))
- j = (y + y//4 + i + 2 - c + c//4) % 7
-
- # p can be from -6 to 56 corresponding to dates 22 March to 23 May
- # (later dates apply to method 2, although 23 May never actually occurs)
- p = i - j + e
- d = 1 + (p + 27 + (p + 6)//40) % 31
- m = 3 + (p + 26)//30
- return datetime.date(int(y), int(m), int(d))
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/packaging/specifiers.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/packaging/specifiers.py
deleted file mode 100644
index 0e218a6f9f75ea2060a8b08d1f1a043fdad68df8..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/packaging/specifiers.py
+++ /dev/null
@@ -1,802 +0,0 @@
-# This file is dual licensed under the terms of the Apache License, Version
-# 2.0, and the BSD License. See the LICENSE file in the root of this repository
-# for complete details.
-
-import abc
-import functools
-import itertools
-import re
-import warnings
-from typing import (
- Callable,
- Dict,
- Iterable,
- Iterator,
- List,
- Optional,
- Pattern,
- Set,
- Tuple,
- TypeVar,
- Union,
-)
-
-from .utils import canonicalize_version
-from .version import LegacyVersion, Version, parse
-
-ParsedVersion = Union[Version, LegacyVersion]
-UnparsedVersion = Union[Version, LegacyVersion, str]
-VersionTypeVar = TypeVar("VersionTypeVar", bound=UnparsedVersion)
-CallableOperator = Callable[[ParsedVersion, str], bool]
-
-
-class InvalidSpecifier(ValueError):
- """
- An invalid specifier was found, users should refer to PEP 440.
- """
-
-
-class BaseSpecifier(metaclass=abc.ABCMeta):
- @abc.abstractmethod
- def __str__(self) -> str:
- """
- Returns the str representation of this Specifier like object. This
- should be representative of the Specifier itself.
- """
-
- @abc.abstractmethod
- def __hash__(self) -> int:
- """
- Returns a hash value for this Specifier like object.
- """
-
- @abc.abstractmethod
- def __eq__(self, other: object) -> bool:
- """
- Returns a boolean representing whether or not the two Specifier like
- objects are equal.
- """
-
- @abc.abstractproperty
- def prereleases(self) -> Optional[bool]:
- """
- Returns whether or not pre-releases as a whole are allowed by this
- specifier.
- """
-
- @prereleases.setter
- def prereleases(self, value: bool) -> None:
- """
- Sets whether or not pre-releases as a whole are allowed by this
- specifier.
- """
-
- @abc.abstractmethod
- def contains(self, item: str, prereleases: Optional[bool] = None) -> bool:
- """
- Determines if the given item is contained within this specifier.
- """
-
- @abc.abstractmethod
- def filter(
- self, iterable: Iterable[VersionTypeVar], prereleases: Optional[bool] = None
- ) -> Iterable[VersionTypeVar]:
- """
- Takes an iterable of items and filters them so that only items which
- are contained within this specifier are allowed in it.
- """
-
-
-class _IndividualSpecifier(BaseSpecifier):
-
- _operators: Dict[str, str] = {}
- _regex: Pattern[str]
-
- def __init__(self, spec: str = "", prereleases: Optional[bool] = None) -> None:
- match = self._regex.search(spec)
- if not match:
- raise InvalidSpecifier(f"Invalid specifier: '{spec}'")
-
- self._spec: Tuple[str, str] = (
- match.group("operator").strip(),
- match.group("version").strip(),
- )
-
- # Store whether or not this Specifier should accept prereleases
- self._prereleases = prereleases
-
- def __repr__(self) -> str:
- pre = (
- f", prereleases={self.prereleases!r}"
- if self._prereleases is not None
- else ""
- )
-
- return f"<{self.__class__.__name__}({str(self)!r}{pre})>"
-
- def __str__(self) -> str:
- return "{}{}".format(*self._spec)
-
- @property
- def _canonical_spec(self) -> Tuple[str, str]:
- return self._spec[0], canonicalize_version(self._spec[1])
-
- def __hash__(self) -> int:
- return hash(self._canonical_spec)
-
- def __eq__(self, other: object) -> bool:
- if isinstance(other, str):
- try:
- other = self.__class__(str(other))
- except InvalidSpecifier:
- return NotImplemented
- elif not isinstance(other, self.__class__):
- return NotImplemented
-
- return self._canonical_spec == other._canonical_spec
-
- def _get_operator(self, op: str) -> CallableOperator:
- operator_callable: CallableOperator = getattr(
- self, f"_compare_{self._operators[op]}"
- )
- return operator_callable
-
- def _coerce_version(self, version: UnparsedVersion) -> ParsedVersion:
- if not isinstance(version, (LegacyVersion, Version)):
- version = parse(version)
- return version
-
- @property
- def operator(self) -> str:
- return self._spec[0]
-
- @property
- def version(self) -> str:
- return self._spec[1]
-
- @property
- def prereleases(self) -> Optional[bool]:
- return self._prereleases
-
- @prereleases.setter
- def prereleases(self, value: bool) -> None:
- self._prereleases = value
-
- def __contains__(self, item: str) -> bool:
- return self.contains(item)
-
- def contains(
- self, item: UnparsedVersion, prereleases: Optional[bool] = None
- ) -> bool:
-
- # Determine if prereleases are to be allowed or not.
- if prereleases is None:
- prereleases = self.prereleases
-
- # Normalize item to a Version or LegacyVersion, this allows us to have
- # a shortcut for ``"2.0" in Specifier(">=2")
- normalized_item = self._coerce_version(item)
-
- # Determine if we should be supporting prereleases in this specifier
- # or not, if we do not support prereleases than we can short circuit
- # logic if this version is a prereleases.
- if normalized_item.is_prerelease and not prereleases:
- return False
-
- # Actually do the comparison to determine if this item is contained
- # within this Specifier or not.
- operator_callable: CallableOperator = self._get_operator(self.operator)
- return operator_callable(normalized_item, self.version)
-
- def filter(
- self, iterable: Iterable[VersionTypeVar], prereleases: Optional[bool] = None
- ) -> Iterable[VersionTypeVar]:
-
- yielded = False
- found_prereleases = []
-
- kw = {"prereleases": prereleases if prereleases is not None else True}
-
- # Attempt to iterate over all the values in the iterable and if any of
- # them match, yield them.
- for version in iterable:
- parsed_version = self._coerce_version(version)
-
- if self.contains(parsed_version, **kw):
- # If our version is a prerelease, and we were not set to allow
- # prereleases, then we'll store it for later in case nothing
- # else matches this specifier.
- if parsed_version.is_prerelease and not (
- prereleases or self.prereleases
- ):
- found_prereleases.append(version)
- # Either this is not a prerelease, or we should have been
- # accepting prereleases from the beginning.
- else:
- yielded = True
- yield version
-
- # Now that we've iterated over everything, determine if we've yielded
- # any values, and if we have not and we have any prereleases stored up
- # then we will go ahead and yield the prereleases.
- if not yielded and found_prereleases:
- for version in found_prereleases:
- yield version
-
-
-class LegacySpecifier(_IndividualSpecifier):
-
- _regex_str = r"""
- (?P(==|!=|<=|>=|<|>))
- \s*
- (?P
- [^,;\s)]* # Since this is a "legacy" specifier, and the version
- # string can be just about anything, we match everything
- # except for whitespace, a semi-colon for marker support,
- # a closing paren since versions can be enclosed in
- # them, and a comma since it's a version separator.
- )
- """
-
- _regex = re.compile(r"^\s*" + _regex_str + r"\s*$", re.VERBOSE | re.IGNORECASE)
-
- _operators = {
- "==": "equal",
- "!=": "not_equal",
- "<=": "less_than_equal",
- ">=": "greater_than_equal",
- "<": "less_than",
- ">": "greater_than",
- }
-
- def __init__(self, spec: str = "", prereleases: Optional[bool] = None) -> None:
- super().__init__(spec, prereleases)
-
- warnings.warn(
- "Creating a LegacyVersion has been deprecated and will be "
- "removed in the next major release",
- DeprecationWarning,
- )
-
- def _coerce_version(self, version: UnparsedVersion) -> LegacyVersion:
- if not isinstance(version, LegacyVersion):
- version = LegacyVersion(str(version))
- return version
-
- def _compare_equal(self, prospective: LegacyVersion, spec: str) -> bool:
- return prospective == self._coerce_version(spec)
-
- def _compare_not_equal(self, prospective: LegacyVersion, spec: str) -> bool:
- return prospective != self._coerce_version(spec)
-
- def _compare_less_than_equal(self, prospective: LegacyVersion, spec: str) -> bool:
- return prospective <= self._coerce_version(spec)
-
- def _compare_greater_than_equal(
- self, prospective: LegacyVersion, spec: str
- ) -> bool:
- return prospective >= self._coerce_version(spec)
-
- def _compare_less_than(self, prospective: LegacyVersion, spec: str) -> bool:
- return prospective < self._coerce_version(spec)
-
- def _compare_greater_than(self, prospective: LegacyVersion, spec: str) -> bool:
- return prospective > self._coerce_version(spec)
-
-
-def _require_version_compare(
- fn: Callable[["Specifier", ParsedVersion, str], bool]
-) -> Callable[["Specifier", ParsedVersion, str], bool]:
- @functools.wraps(fn)
- def wrapped(self: "Specifier", prospective: ParsedVersion, spec: str) -> bool:
- if not isinstance(prospective, Version):
- return False
- return fn(self, prospective, spec)
-
- return wrapped
-
-
-class Specifier(_IndividualSpecifier):
-
- _regex_str = r"""
- (?P(~=|==|!=|<=|>=|<|>|===))
- (?P
- (?:
- # The identity operators allow for an escape hatch that will
- # do an exact string match of the version you wish to install.
- # This will not be parsed by PEP 440 and we cannot determine
- # any semantic meaning from it. This operator is discouraged
- # but included entirely as an escape hatch.
- (?<====) # Only match for the identity operator
- \s*
- [^\s]* # We just match everything, except for whitespace
- # since we are only testing for strict identity.
- )
- |
- (?:
- # The (non)equality operators allow for wild card and local
- # versions to be specified so we have to define these two
- # operators separately to enable that.
- (?<===|!=) # Only match for equals and not equals
-
- \s*
- v?
- (?:[0-9]+!)? # epoch
- [0-9]+(?:\.[0-9]+)* # release
- (?: # pre release
- [-_\.]?
- (a|b|c|rc|alpha|beta|pre|preview)
- [-_\.]?
- [0-9]*
- )?
- (?: # post release
- (?:-[0-9]+)|(?:[-_\.]?(post|rev|r)[-_\.]?[0-9]*)
- )?
-
- # You cannot use a wild card and a dev or local version
- # together so group them with a | and make them optional.
- (?:
- (?:[-_\.]?dev[-_\.]?[0-9]*)? # dev release
- (?:\+[a-z0-9]+(?:[-_\.][a-z0-9]+)*)? # local
- |
- \.\* # Wild card syntax of .*
- )?
- )
- |
- (?:
- # The compatible operator requires at least two digits in the
- # release segment.
- (?<=~=) # Only match for the compatible operator
-
- \s*
- v?
- (?:[0-9]+!)? # epoch
- [0-9]+(?:\.[0-9]+)+ # release (We have a + instead of a *)
- (?: # pre release
- [-_\.]?
- (a|b|c|rc|alpha|beta|pre|preview)
- [-_\.]?
- [0-9]*
- )?
- (?: # post release
- (?:-[0-9]+)|(?:[-_\.]?(post|rev|r)[-_\.]?[0-9]*)
- )?
- (?:[-_\.]?dev[-_\.]?[0-9]*)? # dev release
- )
- |
- (?:
- # All other operators only allow a sub set of what the
- # (non)equality operators do. Specifically they do not allow
- # local versions to be specified nor do they allow the prefix
- # matching wild cards.
- (?=": "greater_than_equal",
- "<": "less_than",
- ">": "greater_than",
- "===": "arbitrary",
- }
-
- @_require_version_compare
- def _compare_compatible(self, prospective: ParsedVersion, spec: str) -> bool:
-
- # Compatible releases have an equivalent combination of >= and ==. That
- # is that ~=2.2 is equivalent to >=2.2,==2.*. This allows us to
- # implement this in terms of the other specifiers instead of
- # implementing it ourselves. The only thing we need to do is construct
- # the other specifiers.
-
- # We want everything but the last item in the version, but we want to
- # ignore suffix segments.
- prefix = ".".join(
- list(itertools.takewhile(_is_not_suffix, _version_split(spec)))[:-1]
- )
-
- # Add the prefix notation to the end of our string
- prefix += ".*"
-
- return self._get_operator(">=")(prospective, spec) and self._get_operator("==")(
- prospective, prefix
- )
-
- @_require_version_compare
- def _compare_equal(self, prospective: ParsedVersion, spec: str) -> bool:
-
- # We need special logic to handle prefix matching
- if spec.endswith(".*"):
- # In the case of prefix matching we want to ignore local segment.
- prospective = Version(prospective.public)
- # Split the spec out by dots, and pretend that there is an implicit
- # dot in between a release segment and a pre-release segment.
- split_spec = _version_split(spec[:-2]) # Remove the trailing .*
-
- # Split the prospective version out by dots, and pretend that there
- # is an implicit dot in between a release segment and a pre-release
- # segment.
- split_prospective = _version_split(str(prospective))
-
- # Shorten the prospective version to be the same length as the spec
- # so that we can determine if the specifier is a prefix of the
- # prospective version or not.
- shortened_prospective = split_prospective[: len(split_spec)]
-
- # Pad out our two sides with zeros so that they both equal the same
- # length.
- padded_spec, padded_prospective = _pad_version(
- split_spec, shortened_prospective
- )
-
- return padded_prospective == padded_spec
- else:
- # Convert our spec string into a Version
- spec_version = Version(spec)
-
- # If the specifier does not have a local segment, then we want to
- # act as if the prospective version also does not have a local
- # segment.
- if not spec_version.local:
- prospective = Version(prospective.public)
-
- return prospective == spec_version
-
- @_require_version_compare
- def _compare_not_equal(self, prospective: ParsedVersion, spec: str) -> bool:
- return not self._compare_equal(prospective, spec)
-
- @_require_version_compare
- def _compare_less_than_equal(self, prospective: ParsedVersion, spec: str) -> bool:
-
- # NB: Local version identifiers are NOT permitted in the version
- # specifier, so local version labels can be universally removed from
- # the prospective version.
- return Version(prospective.public) <= Version(spec)
-
- @_require_version_compare
- def _compare_greater_than_equal(
- self, prospective: ParsedVersion, spec: str
- ) -> bool:
-
- # NB: Local version identifiers are NOT permitted in the version
- # specifier, so local version labels can be universally removed from
- # the prospective version.
- return Version(prospective.public) >= Version(spec)
-
- @_require_version_compare
- def _compare_less_than(self, prospective: ParsedVersion, spec_str: str) -> bool:
-
- # Convert our spec to a Version instance, since we'll want to work with
- # it as a version.
- spec = Version(spec_str)
-
- # Check to see if the prospective version is less than the spec
- # version. If it's not we can short circuit and just return False now
- # instead of doing extra unneeded work.
- if not prospective < spec:
- return False
-
- # This special case is here so that, unless the specifier itself
- # includes is a pre-release version, that we do not accept pre-release
- # versions for the version mentioned in the specifier (e.g. <3.1 should
- # not match 3.1.dev0, but should match 3.0.dev0).
- if not spec.is_prerelease and prospective.is_prerelease:
- if Version(prospective.base_version) == Version(spec.base_version):
- return False
-
- # If we've gotten to here, it means that prospective version is both
- # less than the spec version *and* it's not a pre-release of the same
- # version in the spec.
- return True
-
- @_require_version_compare
- def _compare_greater_than(self, prospective: ParsedVersion, spec_str: str) -> bool:
-
- # Convert our spec to a Version instance, since we'll want to work with
- # it as a version.
- spec = Version(spec_str)
-
- # Check to see if the prospective version is greater than the spec
- # version. If it's not we can short circuit and just return False now
- # instead of doing extra unneeded work.
- if not prospective > spec:
- return False
-
- # This special case is here so that, unless the specifier itself
- # includes is a post-release version, that we do not accept
- # post-release versions for the version mentioned in the specifier
- # (e.g. >3.1 should not match 3.0.post0, but should match 3.2.post0).
- if not spec.is_postrelease and prospective.is_postrelease:
- if Version(prospective.base_version) == Version(spec.base_version):
- return False
-
- # Ensure that we do not allow a local version of the version mentioned
- # in the specifier, which is technically greater than, to match.
- if prospective.local is not None:
- if Version(prospective.base_version) == Version(spec.base_version):
- return False
-
- # If we've gotten to here, it means that prospective version is both
- # greater than the spec version *and* it's not a pre-release of the
- # same version in the spec.
- return True
-
- def _compare_arbitrary(self, prospective: Version, spec: str) -> bool:
- return str(prospective).lower() == str(spec).lower()
-
- @property
- def prereleases(self) -> bool:
-
- # If there is an explicit prereleases set for this, then we'll just
- # blindly use that.
- if self._prereleases is not None:
- return self._prereleases
-
- # Look at all of our specifiers and determine if they are inclusive
- # operators, and if they are if they are including an explicit
- # prerelease.
- operator, version = self._spec
- if operator in ["==", ">=", "<=", "~=", "==="]:
- # The == specifier can include a trailing .*, if it does we
- # want to remove before parsing.
- if operator == "==" and version.endswith(".*"):
- version = version[:-2]
-
- # Parse the version, and if it is a pre-release than this
- # specifier allows pre-releases.
- if parse(version).is_prerelease:
- return True
-
- return False
-
- @prereleases.setter
- def prereleases(self, value: bool) -> None:
- self._prereleases = value
-
-
-_prefix_regex = re.compile(r"^([0-9]+)((?:a|b|c|rc)[0-9]+)$")
-
-
-def _version_split(version: str) -> List[str]:
- result: List[str] = []
- for item in version.split("."):
- match = _prefix_regex.search(item)
- if match:
- result.extend(match.groups())
- else:
- result.append(item)
- return result
-
-
-def _is_not_suffix(segment: str) -> bool:
- return not any(
- segment.startswith(prefix) for prefix in ("dev", "a", "b", "rc", "post")
- )
-
-
-def _pad_version(left: List[str], right: List[str]) -> Tuple[List[str], List[str]]:
- left_split, right_split = [], []
-
- # Get the release segment of our versions
- left_split.append(list(itertools.takewhile(lambda x: x.isdigit(), left)))
- right_split.append(list(itertools.takewhile(lambda x: x.isdigit(), right)))
-
- # Get the rest of our versions
- left_split.append(left[len(left_split[0]) :])
- right_split.append(right[len(right_split[0]) :])
-
- # Insert our padding
- left_split.insert(1, ["0"] * max(0, len(right_split[0]) - len(left_split[0])))
- right_split.insert(1, ["0"] * max(0, len(left_split[0]) - len(right_split[0])))
-
- return (list(itertools.chain(*left_split)), list(itertools.chain(*right_split)))
-
-
-class SpecifierSet(BaseSpecifier):
- def __init__(
- self, specifiers: str = "", prereleases: Optional[bool] = None
- ) -> None:
-
- # Split on , to break each individual specifier into it's own item, and
- # strip each item to remove leading/trailing whitespace.
- split_specifiers = [s.strip() for s in specifiers.split(",") if s.strip()]
-
- # Parsed each individual specifier, attempting first to make it a
- # Specifier and falling back to a LegacySpecifier.
- parsed: Set[_IndividualSpecifier] = set()
- for specifier in split_specifiers:
- try:
- parsed.add(Specifier(specifier))
- except InvalidSpecifier:
- parsed.add(LegacySpecifier(specifier))
-
- # Turn our parsed specifiers into a frozen set and save them for later.
- self._specs = frozenset(parsed)
-
- # Store our prereleases value so we can use it later to determine if
- # we accept prereleases or not.
- self._prereleases = prereleases
-
- def __repr__(self) -> str:
- pre = (
- f", prereleases={self.prereleases!r}"
- if self._prereleases is not None
- else ""
- )
-
- return f""
-
- def __str__(self) -> str:
- return ",".join(sorted(str(s) for s in self._specs))
-
- def __hash__(self) -> int:
- return hash(self._specs)
-
- def __and__(self, other: Union["SpecifierSet", str]) -> "SpecifierSet":
- if isinstance(other, str):
- other = SpecifierSet(other)
- elif not isinstance(other, SpecifierSet):
- return NotImplemented
-
- specifier = SpecifierSet()
- specifier._specs = frozenset(self._specs | other._specs)
-
- if self._prereleases is None and other._prereleases is not None:
- specifier._prereleases = other._prereleases
- elif self._prereleases is not None and other._prereleases is None:
- specifier._prereleases = self._prereleases
- elif self._prereleases == other._prereleases:
- specifier._prereleases = self._prereleases
- else:
- raise ValueError(
- "Cannot combine SpecifierSets with True and False prerelease "
- "overrides."
- )
-
- return specifier
-
- def __eq__(self, other: object) -> bool:
- if isinstance(other, (str, _IndividualSpecifier)):
- other = SpecifierSet(str(other))
- elif not isinstance(other, SpecifierSet):
- return NotImplemented
-
- return self._specs == other._specs
-
- def __len__(self) -> int:
- return len(self._specs)
-
- def __iter__(self) -> Iterator[_IndividualSpecifier]:
- return iter(self._specs)
-
- @property
- def prereleases(self) -> Optional[bool]:
-
- # If we have been given an explicit prerelease modifier, then we'll
- # pass that through here.
- if self._prereleases is not None:
- return self._prereleases
-
- # If we don't have any specifiers, and we don't have a forced value,
- # then we'll just return None since we don't know if this should have
- # pre-releases or not.
- if not self._specs:
- return None
-
- # Otherwise we'll see if any of the given specifiers accept
- # prereleases, if any of them do we'll return True, otherwise False.
- return any(s.prereleases for s in self._specs)
-
- @prereleases.setter
- def prereleases(self, value: bool) -> None:
- self._prereleases = value
-
- def __contains__(self, item: UnparsedVersion) -> bool:
- return self.contains(item)
-
- def contains(
- self, item: UnparsedVersion, prereleases: Optional[bool] = None
- ) -> bool:
-
- # Ensure that our item is a Version or LegacyVersion instance.
- if not isinstance(item, (LegacyVersion, Version)):
- item = parse(item)
-
- # Determine if we're forcing a prerelease or not, if we're not forcing
- # one for this particular filter call, then we'll use whatever the
- # SpecifierSet thinks for whether or not we should support prereleases.
- if prereleases is None:
- prereleases = self.prereleases
-
- # We can determine if we're going to allow pre-releases by looking to
- # see if any of the underlying items supports them. If none of them do
- # and this item is a pre-release then we do not allow it and we can
- # short circuit that here.
- # Note: This means that 1.0.dev1 would not be contained in something
- # like >=1.0.devabc however it would be in >=1.0.debabc,>0.0.dev0
- if not prereleases and item.is_prerelease:
- return False
-
- # We simply dispatch to the underlying specs here to make sure that the
- # given version is contained within all of them.
- # Note: This use of all() here means that an empty set of specifiers
- # will always return True, this is an explicit design decision.
- return all(s.contains(item, prereleases=prereleases) for s in self._specs)
-
- def filter(
- self, iterable: Iterable[VersionTypeVar], prereleases: Optional[bool] = None
- ) -> Iterable[VersionTypeVar]:
-
- # Determine if we're forcing a prerelease or not, if we're not forcing
- # one for this particular filter call, then we'll use whatever the
- # SpecifierSet thinks for whether or not we should support prereleases.
- if prereleases is None:
- prereleases = self.prereleases
-
- # If we have any specifiers, then we want to wrap our iterable in the
- # filter method for each one, this will act as a logical AND amongst
- # each specifier.
- if self._specs:
- for spec in self._specs:
- iterable = spec.filter(iterable, prereleases=bool(prereleases))
- return iterable
- # If we do not have any specifiers, then we need to have a rough filter
- # which will filter out any pre-releases, unless there are no final
- # releases, and which will filter out LegacyVersion in general.
- else:
- filtered: List[VersionTypeVar] = []
- found_prereleases: List[VersionTypeVar] = []
-
- item: UnparsedVersion
- parsed_version: Union[Version, LegacyVersion]
-
- for item in iterable:
- # Ensure that we some kind of Version class for this item.
- if not isinstance(item, (LegacyVersion, Version)):
- parsed_version = parse(item)
- else:
- parsed_version = item
-
- # Filter out any item which is parsed as a LegacyVersion
- if isinstance(parsed_version, LegacyVersion):
- continue
-
- # Store any item which is a pre-release for later unless we've
- # already found a final version or we are accepting prereleases
- if parsed_version.is_prerelease and not prereleases:
- if not filtered:
- found_prereleases.append(item)
- else:
- filtered.append(item)
-
- # If we've found no items except for pre-releases, then we'll go
- # ahead and use the pre-releases
- if not filtered and found_prereleases and prereleases is None:
- return found_prereleases
-
- return filtered
diff --git a/spaces/BraydenMoore/MARCI-NFL-Betting/Templates/index.html b/spaces/BraydenMoore/MARCI-NFL-Betting/Templates/index.html
deleted file mode 100644
index e4d2d353f823a8624500816c5092e7378e84136f..0000000000000000000000000000000000000000
--- a/spaces/BraydenMoore/MARCI-NFL-Betting/Templates/index.html
+++ /dev/null
@@ -1,724 +0,0 @@
-
-
-
-
-
- MARCI - NFL Betting
-
-
-
-
-
- Predictions will begin at the conclusion of Week 1. Bet at your own risk. Know your limits. And most importantly, have fun!
-
-
-
-
-
M A R C I
-
- Moore's Algorithm for Risky Capital Investments
-
- Remember to have fun!
-
- Record through {{ latest_game }}
- Winners: {{ winners_correct }}-{{winners_incorrect}}{{winners_tie}} ({{ winners_return }})
- Over/Unders: {{over_unders_correct}}-{{over_unders_incorrect}}{{over_unders_push}} ({{over_unders_return}})
-
-The Roto Brush tool in After Effects is much faster than conventional rotoscoping, ... Then ... Adobe After Effects Cs4 RotoBrush Plugin aecd9d6ae7. Re: can i ... Download Driver Megapixel 10x Digital Zoom F 385mm Freerar 1fdad05405
-
-
-
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Driver San Francisco BLACK-BOX Repack 3.2GB[.Dude] Pc Game.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Driver San Francisco BLACK-BOX Repack 3.2GB[.Dude] Pc Game.md
deleted file mode 100644
index 6792a421f4dd392a85806f31d6f9a19598222919..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Driver San Francisco BLACK-BOX Repack 3.2GB[.Dude] Pc Game.md
+++ /dev/null
@@ -1,21 +0,0 @@
-
-
Driver San Francisco BLACK-BOX Repack 3.2GB[.Dude] Pc Game Review
-
If you are looking for a thrilling and action-packed racing game that will keep you on the edge of your seat, then you should check out Driver San Francisco BLACK-BOX Repack 3.2GB[.Dude] Pc Game. This is a highly compressed version of the original game that has been repacked by BLACK-BOX, a group of skilled hackers who specialize in cracking and repacking games. The repack reduces the size of the game from 10GB to 3.2GB, making it easier to download and install.
-
Driver San Francisco BLACK-BOX Repack 3.2GB[.Dude] Pc Game
Driver San Francisco is the fifth installment in the Driver series, which is known for its realistic driving physics, cinematic car chases, and open-world exploration. The game is set in San Francisco, where you play as John Tanner, a detective who has the ability to shift into any car in the city. You can use this power to pursue criminals, complete missions, or just have fun driving around the iconic landmarks of San Francisco.
-
The game features over 120 licensed vehicles, from muscle cars to sports cars, that you can drive and customize. You can also engage in online multiplayer modes, such as tag, trailblazer, or cops and robbers, where you can compete or cooperate with other players. The game has stunning graphics, realistic sound effects, and a dynamic soundtrack that adapts to your driving style.
-
Driver San Francisco BLACK-BOX Repack 3.2GB[.Dude] Pc Game is a must-have for any racing fan who wants to experience the thrill of driving in one of the most beautiful and diverse cities in the world. You can download it from https://www.blackboxrepack.com/driver-san-francisco-black-box/ and enjoy the game without any hassle.
-
-
How to Install Driver San Francisco BLACK-BOX Repack 3.2GB[.Dude] Pc Game
-
Installing Driver San Francisco BLACK-BOX Repack 3.2GB[.Dude] Pc Game is very easy and straightforward. Just follow these simple steps and you will be ready to play in no time.
Extract the repack using WinRAR or 7-Zip. You will get a folder named "Driver San Francisco BLACK-BOX Repack 3.2GB[.Dude]".
-
Run the setup.exe file and follow the instructions. You can choose the installation directory and language of the game.
-
Wait for the installation to finish. It may take some time depending on your system specifications.
-
Run the game from the desktop shortcut or the start menu. You don't need to crack or activate the game as it is already done by BLACK-BOX.
-
-
Congratulations! You have successfully installed Driver San Francisco BLACK-BOX Repack 3.2GB[.Dude] Pc Game. Enjoy the game and don't forget to share your feedback with us.
d5da3c52bf
-
-
\ No newline at end of file
diff --git a/spaces/lsy641/distinct/tests.py b/spaces/lsy641/distinct/tests.py
deleted file mode 100644
index 9abfbc4317317b1ecb356b9cc4d0a6d892301d6c..0000000000000000000000000000000000000000
--- a/spaces/lsy641/distinct/tests.py
+++ /dev/null
@@ -1,14 +0,0 @@
-test_cases = [
- {
- "predictions": ["Hi.", "I'm sorry to hear that", "I don't know"],
- "result": {"metric_score": 0}
- },
- {
- "predictions": ["Hi.", "I'm sorry to hear that", "I don't know"],
- "result": {"metric_score": 1}
- },
- {
- "predictions": ["Hi.", "I'm sorry to hear that", "I don't know"],
- "result": {"metric_score": 0.5}
- }
-]
\ No newline at end of file
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/system/cuda/detail/transform.h b/spaces/ma-xu/LIVE/thrust/thrust/system/cuda/detail/transform.h
deleted file mode 100644
index 053fe9095a9bba47a05cf8b21c4a1954107685aa..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/system/cuda/detail/transform.h
+++ /dev/null
@@ -1,426 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2016, NVIDIA CORPORATION. All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- * * Redistributions of source code must retain the above copyright
- * notice, this list of conditions and the following disclaimer.
- * * Redistributions in binary form must reproduce the above copyright
- * notice, this list of conditions and the following disclaimer in the
- * documentation and/or other materials provided with the distribution.
- * * Neither the name of the NVIDIA CORPORATION nor the
- * names of its contributors may be used to endorse or promote products
- * derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
- * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
- * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
- * ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-#pragma once
-
-
-#if THRUST_DEVICE_COMPILER == THRUST_DEVICE_COMPILER_NVCC
-#include
-
-#include
-#include
-#include
-#include
-
-namespace thrust
-{
-
-namespace cuda_cub {
-
-
-namespace __transform {
-
- struct no_stencil_tag
- {
- };
-
- struct always_true_predicate
- {
- template
- bool THRUST_DEVICE_FUNCTION operator()(T const &) const
- {
- return true;
- }
- };
-
- template
- struct unary_transform_f
- {
- InputIt input;
- OutputIt output;
- StencilIt stencil;
- TransformOp op;
- Predicate pred;
-
- THRUST_FUNCTION
- unary_transform_f(InputIt input_,
- OutputIt output_,
- StencilIt stencil_,
- TransformOp op_,
- Predicate pred_)
- : input(input_),
- output(output_),
- stencil(stencil_),
- op(op_),
- pred(pred_) {}
-
- template
- void THRUST_DEVICE_FUNCTION operator()(Size idx)
- {
- if (pred(raw_reference_cast(stencil[idx])))
- output[idx] = op(raw_reference_cast(input[idx]));
- }
- }; // struct unary_transform_stencil_f
-
- template
- struct unary_transform_f
- {
- InputIt input;
- OutputIt output;
- TransformOp op;
- Predicate pred;
-
- THRUST_FUNCTION
- unary_transform_f(InputIt input_,
- OutputIt output_,
- no_stencil_tag,
- TransformOp op_,
- Predicate pred_)
- : input(input_), output(output_), op(op_), pred(pred_) {}
-
- template
- void THRUST_DEVICE_FUNCTION operator()(Size idx)
- {
- if (pred(raw_reference_cast(input[idx])))
- output[idx] = op(raw_reference_cast(input[idx]));
- }
- }; // struct unary_transform_f
-
- template
- struct binary_transform_f
- {
- InputIt1 input1;
- InputIt2 input2;
- OutputIt output;
- StencilIt stencil;
- TransformOp op;
- Predicate pred;
-
- THRUST_FUNCTION
- binary_transform_f(InputIt1 input1_,
- InputIt2 input2_,
- OutputIt output_,
- StencilIt stencil_,
- TransformOp op_,
- Predicate pred_)
- : input1(input1_),
- input2(input2_),
- output(output_),
- stencil(stencil_),
- op(op_),
- pred(pred_) {}
-
- template
- void THRUST_DEVICE_FUNCTION operator()(Size idx)
- {
- if (pred(raw_reference_cast(stencil[idx])))
- output[idx] = op(raw_reference_cast(input1[idx]),
- raw_reference_cast(input2[idx]));
- }
- }; // struct binary_transform_stencil_f
-
- template
- struct binary_transform_f
- {
- InputIt1 input1;
- InputIt2 input2;
- OutputIt output;
- TransformOp op;
- Predicate pred;
-
- THRUST_FUNCTION
- binary_transform_f(InputIt1 input1_,
- InputIt2 input2_,
- OutputIt output_,
- no_stencil_tag ,
- TransformOp op_,
- Predicate pred_)
- : input1(input1_),
- input2(input2_),
- output(output_),
- op(op_),
- pred(pred_) {}
-
- template
- void THRUST_DEVICE_FUNCTION operator()(Size idx)
- {
- if (pred(raw_reference_cast(input1[idx])))
- output[idx] = op(raw_reference_cast(input1[idx]),
- raw_reference_cast(input2[idx]));
- }
- }; // struct binary_transform_f
-
- template
- OutputIt THRUST_FUNCTION
- unary(Policy & policy,
- InputIt items,
- OutputIt result,
- Size num_items,
- StencilIt stencil,
- TransformOp transform_op,
- Predicate predicate)
- {
- if (num_items == 0)
- return result;
-
- typedef unary_transform_f
- unary_transform_t;
-
- cuda_cub::parallel_for(policy,
- unary_transform_t(items,
- result,
- stencil,
- transform_op,
- predicate),
- num_items);
-
- cuda_cub::throw_on_error(
- cuda_cub::synchronize(policy)
- , "transform: failed to synchronize"
- );
-
- return result + num_items;
- }
-
- template
- OutputIt THRUST_FUNCTION
- binary(Policy & policy,
- InputIt1 items1,
- InputIt2 items2,
- OutputIt result,
- Size num_items,
- StencilIt stencil,
- TransformOp transform_op,
- Predicate predicate)
- {
- if (num_items == 0)
- return result;
-
- typedef binary_transform_f
- binary_transform_t;
-
- cuda_cub::parallel_for(policy,
- binary_transform_t(items1,
- items2,
- result,
- stencil,
- transform_op,
- predicate),
- num_items);
-
- cuda_cub::throw_on_error(
- cuda_cub::synchronize(policy)
- , "transform: failed to synchronize"
- );
-
- return result + num_items;
- }
-
-} // namespace __transform
-
-//-------------------------
-// Thrust API entry points
-//-------------------------
-
-//-------------------------
-// one input data stream
-//-------------------------
-
-template
-OutputIt THRUST_FUNCTION
-transform_if(execution_policy &policy,
- InputIt first,
- InputIt last,
- StencilInputIt stencil,
- OutputIt result,
- TransformOp transform_op,
- Predicate predicate)
-{
- typedef typename iterator_traits::difference_type size_type;
- size_type num_items = static_cast(thrust::distance(first, last));
- return __transform::unary(policy,
- first,
- result,
- num_items,
- stencil,
- transform_op,
- predicate);
-} // func transform_if
-
-template
-OutputIt THRUST_FUNCTION
-transform_if(execution_policy &policy,
- InputIt first,
- InputIt last,
- OutputIt result,
- TransformOp transform_op,
- Predicate predicate)
-{
- return cuda_cub::transform_if(policy,
- first,
- last,
- __transform::no_stencil_tag(),
- result,
- transform_op,
- predicate);
-} // func transform_if
-
-template
-OutputIt THRUST_FUNCTION
-transform(execution_policy &policy,
- InputIt first,
- InputIt last,
- OutputIt result,
- TransformOp transform_op)
-{
- return cuda_cub::transform_if(policy,
- first,
- last,
- result,
- transform_op,
- __transform::always_true_predicate());
-} // func transform
-
-//-------------------------
-// two input data streams
-//-------------------------
-
-
-template
-OutputIt THRUST_FUNCTION
-transform_if(execution_policy &policy,
- InputIt1 first1,
- InputIt1 last1,
- InputIt2 first2,
- StencilInputIt stencil,
- OutputIt result,
- TransformOp transform_op,
- Predicate predicate)
-{
- typedef typename iterator_traits::difference_type size_type;
- size_type num_items = static_cast(thrust::distance(first1, last1));
- return __transform::binary(policy,
- first1,
- first2,
- result,
- num_items,
- stencil,
- transform_op,
- predicate);
-} // func transform_if
-
-template
-OutputIt THRUST_FUNCTION
-transform(execution_policy &policy,
- InputIt1 first1,
- InputIt1 last1,
- InputIt2 first2,
- OutputIt result,
- TransformOp transform_op)
-{
- return cuda_cub::transform_if(policy,
- first1,
- last1,
- first2,
- __transform::no_stencil_tag(),
- result,
- transform_op,
- __transform::always_true_predicate());
-} // func transform
-
-} // namespace cuda_cub
-
-} // end namespace thrust
-#endif
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/system/detail/adl/scan_by_key.h b/spaces/ma-xu/LIVE/thrust/thrust/system/detail/adl/scan_by_key.h
deleted file mode 100644
index 94f73503cac55cb65419bfd81dc9e6c9e7e63c0c..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/system/detail/adl/scan_by_key.h
+++ /dev/null
@@ -1,44 +0,0 @@
-/*
- * Copyright 2008-2013 NVIDIA Corporation
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include
-
-// the purpose of this header is to #include the scan_by_key.h header
-// of the sequential, host, and device systems. It should be #included in any
-// code which uses adl to dispatch scan_by_key
-
-#include
-
-// SCons can't see through the #defines below to figure out what this header
-// includes, so we fake it out by specifying all possible files we might end up
-// including inside an #if 0.
-#if 0
-#include
-#include
-#include
-#include
-#endif
-
-#define __THRUST_HOST_SYSTEM_SCAN_BY_KEY_HEADER <__THRUST_HOST_SYSTEM_ROOT/detail/scan_by_key.h>
-#include __THRUST_HOST_SYSTEM_SCAN_BY_KEY_HEADER
-#undef __THRUST_HOST_SYSTEM_SCAN_BY_KEY_HEADER
-
-#define __THRUST_DEVICE_SYSTEM_SCAN_BY_KEY_HEADER <__THRUST_DEVICE_SYSTEM_ROOT/detail/scan_by_key.h>
-#include __THRUST_DEVICE_SYSTEM_SCAN_BY_KEY_HEADER
-#undef __THRUST_DEVICE_SYSTEM_SCAN_BY_KEY_HEADER
-
diff --git a/spaces/matthoffner/open-codetree/styles/customlib/_customMonacoEditor.css b/spaces/matthoffner/open-codetree/styles/customlib/_customMonacoEditor.css
deleted file mode 100644
index 4077062ec5a94ab59a777f372800c70372e1ceab..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/open-codetree/styles/customlib/_customMonacoEditor.css
+++ /dev/null
@@ -1,202 +0,0 @@
-/* MonacoHighlighter ============================================================================ */
-.editor-wrapper .mtk1 {
- color: #d4d4d4;
-}
-.editor-wrapper .mtk2 {
- color: #1e1e1e;
-}
-.editor-wrapper .mtk3 {
- color: #000080;
-}
-.editor-wrapper .mtk4 {
- color: #6a9955;
-}
-.editor-wrapper .mtk5 {
- color: #569cd6;
-}
-.editor-wrapper .mtk6 {
- color: #b5cea8;
-}
-.editor-wrapper .mtk7 {
- color: #646695;
-}
-.editor-wrapper .mtk8 {
- color: #c586c0;
-}
-.editor-wrapper .mtk9 {
- color: #9cdcfe;
-}
-.editor-wrapper .mtk10 {
- color: #f44747;
-}
-.editor-wrapper .mtk11 {
- color: #ce9178;
-}
-.editor-wrapper .mtk12 {
- color: #6796e6;
-}
-.editor-wrapper .mtk13 {
- color: #808080;
-}
-.editor-wrapper .mtk14 {
- color: #d16969;
-}
-.editor-wrapper .mtk15 {
- color: #dcdcaa;
-}
-.editor-wrapper .mtk16 {
- color: #4ec9b0;
-}
-.editor-wrapper .mtk17 {
- color: #c586c0;
-}
-.editor-wrapper .mtk18 {
- color: #4fc1ff;
-}
-.editor-wrapper .mtk19 {
- color: #c8c8c8;
-}
-.editor-wrapper .mtk20 {
- color: #cd9731;
-}
-.editor-wrapper .mtk21 {
- color: #b267e6;
-}
-.editor-wrapper .mtki {
- font-style: italic;
-}
-.editor-wrapper .mtkb {
- font-weight: bold;
-}
-.editor-wrapper .mtku {
- text-decoration: underline;
- text-underline-position: under;
-}
-
-.editor-wrapper .mtk100.Identifier.JsxElement.Bracket {
- color: #0080ff;
-}
-
-.editor-wrapper .mtk1000.Identifier.JsxOpeningElement.Bracket {
- color: #808080;
- font-weight: bold;
-}
-
-.editor-wrapper .mtk1001.Identifier.JsxClosingElement.Bracket {
- color: #808080;
- font-weight: lighter;
-}
-
-.editor-wrapper .mtk101.Identifier.JsxOpeningElement.Identifier {
- color: #569cd6;
-}
-
-.editor-wrapper .mtk102.Identifier.JsxClosingElement.Identifier {
- color: #569cd6;
- font-weight: lighter;
-}
-
-.editor-wrapper .mtk103.Identifier.JsxAttribute.Identifier {
- color: #9cdcfe;
-}
-
-.editor-wrapper .mtk104.JsxElement.JsxText {
- color: darkgoldenrod;
-}
-
-.editor-wrapper .mtk105.glyph.Identifier.JsxElement {
- background: #61dafb;
- opacity: 0.25;
-}
-
-.editor-wrapper .mtk12.Identifier.JsxExpression.JsxClosingElement {
- color: #ec5f67;
-}
-
-.editor-wrapper .mtk12.Identifier.JsxSelfClosingElement {
- color: #ec5f67;
-}
-.editor-wrapper .mtk12.Identifier.VariableStatement.JsxClosingElement {
- color: #ec5f67 !important;
-}
-.editor-wrapper .mtk12.VariableStatement.JsxSelfClosingElement.Identifier {
- color: #ec5f67;
-}
-.editor-wrapper .mtk12.Identifier.JsxAttribute.VariableDeclaration {
- color: crimson;
-}
-.editor-wrapper .mtk12.JsxExpression.VariableStatement {
- color: #fac863;
-}
-.editor-wrapper .mtk12.VariableStatement.JsxSelfClosingElement {
- color: #ede0e0;
-}
-.editor-wrapper .mtk12.VariableStatement.JsxClosingElement {
- color: #ede0e0;
-}
-.editor-wrapper .JsxText {
- color: #0c141f;
-}
-
-/* MonacoJSXHighlighter ============================================================================ */
-.JSXElement.JSXIdentifier {
- color: #e06c75 !important;
-}
-
-.JSXElement.JSXBracket {
- color: #d4d4d4 !important;
-}
-
-.JSXElement.JSXText {
- color: #d4d4d4 !important;
-}
-
-.JSXElement.JSXGlyph {
- background: cyan;
- opacity: 0.25;
-}
-
-.JSXOpeningFragment.JSXBracket {
- color: darkorange;
- font-weight: bold;
-}
-
-.JSXClosingFragment.JSXBracket {
- color: darkorange;
- font-weight: bold;
-}
-
-.JSXOpeningElement.JSXBracket {
- color: darkorange;
- font-weight: bold;
-}
-
-.JSXOpeningElement.JSXIdentifier {
- color: #e06c75 !important;
-}
-
-.JSXClosingElement.JSXBracket {
- color: darkorange;
- font-weight: lighter;
-}
-
-.JSXClosingElement.JSXIdentifier {
- color: #e06c75 !important;
- font-weight: lighter;
-}
-
-.JSXAttribute.JSXIdentifier {
- color: steelblue;
-}
-
-.JSXExpressionContainer.JSXBracket {
- color: darkorange;
-}
-
-.JSXSpreadChild.JSXBracket {
- color: darkorange;
-}
-
-.JSXSpreadAttribute.JSXBracket {
- color: darkorange;
-}
\ No newline at end of file
diff --git a/spaces/matthoffner/starchat-ui/components/Markdown/CodeBlock.tsx b/spaces/matthoffner/starchat-ui/components/Markdown/CodeBlock.tsx
deleted file mode 100644
index efdea3624ce19a6aa55c9bc8d4c17ea85daf8968..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/starchat-ui/components/Markdown/CodeBlock.tsx
+++ /dev/null
@@ -1,137 +0,0 @@
-import { IconCheck, IconClipboard, IconDownload, IconCaretRight } from '@tabler/icons-react';
-import { FC, memo, useEffect, useRef, useState } from 'react';
-import { Prism as SyntaxHighlighter } from 'react-syntax-highlighter';
-import { oneDark } from 'react-syntax-highlighter/dist/cjs/styles/prism';
-
-import { useTranslation } from 'next-i18next';
-
-import {
- generateRandomString,
- programmingLanguages,
-} from '@/utils/app/codeblock';
-
-interface Props {
- language: string;
- value: string;
-}
-
-const IframeContent: FC = () => {
- useEffect(() => {
- window.addEventListener("message", ({ data: { code, language } }) => {
- if (language === 'html') {
- document.body.innerHTML = code;
- } else if (language === 'javascript') {
- eval(code);
- }
- });
- }, []);
- return null;
-};
-
-export const CodeBlock: FC = memo(({ language, value }) => {
- const { t } = useTranslation('markdown');
- const [isCopied, setIsCopied] = useState(false);
- const [isRunning, setIsRunning] = useState(false);
- const iframeRef = useRef(null);
-
- const toggleRunCode = () => {
- setIsRunning(!isRunning);
- };
-
- useEffect(() => {
- if (isRunning && iframeRef.current) {
- iframeRef.current.contentWindow?.postMessage({ code: value, language }, "*");
- }
- }, [isRunning, value, language]);
-
- const copyToClipboard = () => {
- if (!navigator.clipboard || !navigator.clipboard.writeText) {
- return;
- }
-
- navigator.clipboard.writeText(value).then(() => {
- setIsCopied(true);
-
- setTimeout(() => {
- setIsCopied(false);
- }, 2000);
- });
- };
- const downloadAsFile = () => {
- const fileExtension = programmingLanguages[language] || '.file';
- const suggestedFileName = `file-${generateRandomString(
- 3,
- true,
- )}${fileExtension}`;
- const fileName = window.prompt(
- t('Enter file name') || '',
- suggestedFileName,
- );
-
- if (!fileName) {
- // user pressed cancel on prompt
- return;
- }
-
- const blob = new Blob([value], { type: 'text/plain' });
- const url = URL.createObjectURL(blob);
- const link = document.createElement('a');
- link.download = fileName;
- link.href = url;
- link.style.display = 'none';
- document.body.appendChild(link);
- link.click();
- document.body.removeChild(link);
- URL.revokeObjectURL(url);
- };
- return (
-
- );
-});
-CodeBlock.displayName = 'CodeBlock';
diff --git a/spaces/merve/anonymization/public/third_party/seedrandom.min.js b/spaces/merve/anonymization/public/third_party/seedrandom.min.js
deleted file mode 100644
index 44073008bfb9d3ef533091d4b72db165c8071e84..0000000000000000000000000000000000000000
--- a/spaces/merve/anonymization/public/third_party/seedrandom.min.js
+++ /dev/null
@@ -1,2 +0,0 @@
-// https://github.com/davidbau/seedrandom Copyright 2019 David Bau
-!function(a,b){var l,c=eval("this"),d=256,g="random",h=b.pow(d,6),i=b.pow(2,52),j=2*i,k=d-1;function m(r,t,e){var u=[],f=q(function n(r,t){var e,o=[],i=typeof r;if(t&&"object"==i)for(e in r)try{o.push(n(r[e],t-1))}catch(n){}return o.length?o:"string"==i?r:r+"\0"}((t=1==t?{entropy:!0}:t||{}).entropy?[r,s(a)]:null==r?function(){try{var n;return l&&(n=l.randomBytes)?n=n(d):(n=new Uint8Array(d),(c.crypto||c.msCrypto).getRandomValues(n)),s(n)}catch(n){var r=c.navigator,t=r&&r.plugins;return[+new Date,c,t,c.screen,s(a)]}}():r,3),u),p=new n(u),m=function(){for(var n=p.g(6),r=h,t=0;n>>=1;return(n+t)/r};return m.int32=function(){return 0|p.g(4)},m.quick=function(){return p.g(4)/4294967296},m.double=m,q(s(p.S),a),(t.pass||e||function(n,r,t,e){return e&&(e.S&&o(e,p),n.state=function(){return o(p,{})}),t?(b[g]=n,r):n})(m,f,"global"in t?t.global:this==b,t.state)}function n(n){var r,t=n.length,u=this,e=0,o=u.i=u.j=0,i=u.S=[];for(t||(n=[t++]);e>>0,s=(e*=s)>>>0,s+=4294967296*(e-=s)}return 2.3283064365386963e-10*(s>>>0)}}();t.next=function(){var n=2091639*t.s0+2.3283064365386963e-10*t.c;return t.s0=t.s1,t.s1=t.s2,t.s2=n-(t.c=0|n)},t.c=1,t.s0=e(" "),t.s1=e(" "),t.s2=e(" "),t.s0-=e(n),t.s0<0&&(t.s0+=1),t.s1-=e(n),t.s1<0&&(t.s1+=1),t.s2-=e(n),t.s2<0&&(t.s2+=1),e=null}function o(n,t){return t.c=n.c,t.s0=n.s0,t.s1=n.s1,t.s2=n.s2,t}function s(n,t){var e=new u(n),s=t&&t.state,r=e.next;return r.int32=function(){return 4294967296*e.next()|0},r.double=function(){return r()+11102230246251565e-32*(2097152*r()|0)},r.quick=r,s&&("object"==typeof s&&o(s,e),r.state=function(){return o(e,{})}),r}t&&t.exports?t.exports=s:e&&e.amd?e(function(){return s}):this.alea=s}(0,"object"==typeof module&&module,"function"==typeof define&&define);
\ No newline at end of file
diff --git a/spaces/merve/uncertainty-calibration/source/anonymization/make-students.js b/spaces/merve/uncertainty-calibration/source/anonymization/make-students.js
deleted file mode 100644
index 4406024eb9e398a4eaedc2b725eaf4a56e625e16..0000000000000000000000000000000000000000
--- a/spaces/merve/uncertainty-calibration/source/anonymization/make-students.js
+++ /dev/null
@@ -1,184 +0,0 @@
-window.makeStudents = function(){
- var seed = new Math.seedrandom('12fbsab56')
- var rand = d3.randomUniform.source(seed)(0, 1)
-
- var ncols = 12
-
- var allStudents = d3.range(756).map(i => {
- var age = ages[Math.floor(rand()*ages.length)]
- var state = states[Math.floor(rand()*states.length)]
- var season = Math.floor(rand()*4)
- var heads = rand() < .5
-
- if (rand() < .1) state = 'NY'
- if (rand() < .5 && state == 'RI') state = states[Math.floor(rand()*states.length)]
- if (rand() < .5 && state == 'CT') state = states[Math.floor(rand()*states.length)]
-
- var coinVals = d3.range(300).map(rand).slice(0, 200)
-
- return {age, state, i, pos: {}, group: {}, season, heads, coinVals, isAdditionalStudent: true}
- })
-
- var students = allStudents.slice(0, 144)
- students.forEach(student => student.isAdditionalStudent = false)
-
- students.all = allStudents
- students.all.forEach((d, i) => {
- var x = (i % 25)/25*c.width
- var y = ~~(i/25)/25*c.width
- d.pos.all = [x, y]
- })
-
- var {bw, ageScale, stateScale} = axii
- _.sortBy(students, d => -d.age).forEach((d, i) => {
- var x = (i % ncols)/(ncols - 1)*c.width
- var y = ~~(i/ncols)/(ncols - 1)*c.width
- d.pos.grid = [x, y]
- scale = .6
- d.pos.smallGrid = [x * scale + 90, y * scale]
- })
-
- // Set half the student to have plagerized.
- var studentsPlagerizedArray = _.sortBy(d3.range(students.length).map(i => i % 2 == 0), () => rand())
- // var remainingPlagerizedArray = _.sortBy(d3.range(allStudents.length - students.length).map(i => i % 2 == 0), () => rand())
- remainingPlagerizedArray = d3.range(students.all.length).map(i => i % 2 == 1)
- var plagerizedArray = studentsPlagerizedArray.concat(remainingPlagerizedArray)
- students.all.forEach((d, i) => d.plagerized = plagerizedArray[i])
-
- students.byAge = d3.nestBy(students, d => d.age)
- students.byAge.forEach(age => {
- age.forEach((d, i) => {
- d.pos.age = [i*10, ageScale(d.age) + bw]
- })
- })
- students.byAgeState = d3.nestBy(students, d => d.age + d.state)
- students.byAgeState.forEach(group => {
- var d0 = group.d0 = group[0]
- group.pos = [bw + stateScale(d0.state), bw + ageScale(d0.age)]
-
- var angle = Math.PI*(3 - Math.sqrt(5))*(1 + Math.random()*.05 - .05/2)
- group.forEach((d, i) => {
- d.pos.ageState = addVec(phyllotaxis(i, 10.5, angle), group.pos)
- d.group.ageState = group
- })
- })
-
- students.byAgeStateSeason = d3.nestBy(students, d => d.age + d.state + d.season)
- students.byAgeStateSeason.forEach(group => {
- var d0 = group.d0 = group[0]
- group.pos = [bw + stateScale(d0.state), bw*d0.season/2 + ageScale(d0.age)]
-
- group.forEach((d, i) => {
- d.pos.ageStateSeason = addVec([i*11 - group.length*11/2 + 6, 12], group.pos)
- d.group.ageStateSeason = group
- })
- })
-
-
- students.updateHeadsPos = function(){
- students.byHeads = d3.nestBy(students, d => d.coinVals[estimates.active.index] < sliders.headsProb)
- students.byHeads.forEach(group => {
- group.pos = [group.key == 'true' ? c.width/4 -15 : c.width/4*3 +15, c.height/2]
-
- group.forEach((d, i) => {
- d.pos.heads = addVec(phyllotaxis(i, 12), group.pos)
- d.group.heads = group
- })
- })
- }
-
- students.plagerizedGroup = d3.nestBy(_.sortBy(students.all, d => d.plagerized), d => d.plagerized)
- students.plagerizedGroup.forEach((group, groupIndex) => {
- var d0 = group.d0 = group[0]
- var offset = -20
- group.pos = [(d0.plagerized ? c.width/2 + offset : c.width/2 - offset), c.height/2 - 80]
-
-
- var getOrderedPositions = function() {
- positions = []
-
- var step = 25
- var top = 0
- var bottom = 0
- var right = 0
-
- var addAbove = function(dirPositive=true) {
- var y = (top + 1) * step
- var x = 0
- while (x <= right * step) {
- positions.push([dirPositive ? x: (right * step - x), y])
- x += step
- }
- top++
- }
-
- var addRight = function(dirPositive=true) {
- var x = (right + 1) * step
- var y = bottom * step
- while (y <= top * step) {
- positions.push([x, dirPositive ? y: -y])
- y += step
- }
- right++
- }
-
- var addBelow = function(dirPositive=true) {
- var y = (bottom - 1) * step
- var x = 0
- while (x <= right * step) {
- positions.push([dirPositive ? x: (right * step - x), y])
- x += step
- }
- bottom--
- }
-
- var addForward = function() {
- addAbove(true)
- addRight(false)
- addBelow(false)
- }
-
- var addBackward = function() {
- addBelow(true)
- addRight(true)
- addAbove(false)
- }
-
- isForward = true
- while(positions.length < students.all.length) {
- if (positions.length === 0) {
- positions.push([0, 0])
- addRight()
- addBelow()
- } else {
- if (isForward) {
- addForward()
- } else {
- addBackward()
- }
- isForward = !isForward
- }
- }
- return positions
- }
-
- var populationPositions = getOrderedPositions()
- var reversePositions = populationPositions.map(pos => [-pos[0], pos[1]])
-
- group.forEach((d, i) => {
- var x = (i % 7)/20*c.width
- var y = ~~(i/7)/20*c.width
- // d.pos.plagerized = addVec([x, y], group.pos)
- d.pos.plagerizedShifted = addVec([x, y - 50], group.pos)
- d.group.plagerized = group
-
- d.pos.plagerizedShifted = addVec((groupIndex === 0) ? populationPositions[i]: reversePositions[i], group.pos)
- })
- })
-
-
- students.rand = rand
- return students
-}
-
-if (window.init) window.init()
diff --git a/spaces/mikeee/radiobee-dev/tests/test_text2lists_bug2.py b/spaces/mikeee/radiobee-dev/tests/test_text2lists_bug2.py
deleted file mode 100644
index 428a85d05903ce09c50dade72827913a9b9911dd..0000000000000000000000000000000000000000
--- a/spaces/mikeee/radiobee-dev/tests/test_text2lists_bug2.py
+++ /dev/null
@@ -1,14 +0,0 @@
-"""Test text2lists."""
-from pathlib import Path
-from radiobee.loadtext import loadtext
-from radiobee.text2lists import text2lists
-
-
-def test_text2lists_bug2():
- r"""Test text2lists data\问题2测试文件.txt."""
- filename = r"data\问题2测试文件.txt"
- textbug2 = loadtext(filename) # noqa
- l1, l2 = text2lists(textbug2)
-
- assert len(l1) == 5
- assert len(l2) == 4
diff --git a/spaces/miyaaa666/bingo/src/lib/hooks/use-enter-submit.tsx b/spaces/miyaaa666/bingo/src/lib/hooks/use-enter-submit.tsx
deleted file mode 100644
index d66b2d3253baff164235d4ca791aae6d84721835..0000000000000000000000000000000000000000
--- a/spaces/miyaaa666/bingo/src/lib/hooks/use-enter-submit.tsx
+++ /dev/null
@@ -1,23 +0,0 @@
-import { useRef, type RefObject } from 'react'
-
-export function useEnterSubmit(): {
- formRef: RefObject
- onKeyDown: (event: React.KeyboardEvent) => void
-} {
- const formRef = useRef(null)
-
- const handleKeyDown = (
- event: React.KeyboardEvent
- ): void => {
- if (
- event.key === 'Enter' &&
- !event.shiftKey &&
- !event.nativeEvent.isComposing
- ) {
- formRef.current?.requestSubmit()
- event.preventDefault()
- }
- }
-
- return { formRef, onKeyDown: handleKeyDown }
-}
diff --git a/spaces/miyaaa666/bingo/src/lib/isomorphic/index.ts b/spaces/miyaaa666/bingo/src/lib/isomorphic/index.ts
deleted file mode 100644
index 738dc92f74079ab762d584fb7422a8c8c3b61547..0000000000000000000000000000000000000000
--- a/spaces/miyaaa666/bingo/src/lib/isomorphic/index.ts
+++ /dev/null
@@ -1,17 +0,0 @@
-'use client'
-
-import Default from './browser'
-
-let exportsModel: any = {}
-
-if (process.browser) {
- Object.assign(exportsModel, require('./browser').default)
-} else {
- Object.assign(exportsModel, require('./node').default)
-}
-
-export default exportsModel! as typeof Default
-
-export const fetch: typeof Default.fetch = exportsModel!.fetch
-export const WebSocket: typeof Default.WebSocket = exportsModel!.WebSocket
-export const debug: typeof Default.debug = exportsModel!.debug
diff --git a/spaces/ml-energy/leaderboard/docs/colosseum_top.md b/spaces/ml-energy/leaderboard/docs/colosseum_top.md
deleted file mode 100644
index 9b60f1d6a10b088bc954a1ea40014f0438c3bd33..0000000000000000000000000000000000000000
--- a/spaces/ml-energy/leaderboard/docs/colosseum_top.md
+++ /dev/null
@@ -1,12 +0,0 @@
-> Enter the ML.ENERGY Colosseum, where language models duel with intellect, and your judgment tips the scales of victory.
-
-### Rules of the Colosseum
-
-- As the spectator, you'll decide the fates of two anonymous language models -- our gladiators.
-- Your role is twofold: First, you vote for the model that delivered the best response to your prompt.
-- Next, mighty [Zeus](https://ml.energy/zeus) will reveal which language model consumed more energy. Evaluate if its performance justified the energy consumption.
-- Only after you cast votes will the models' identities be unveiled.
-
-### Terms of use
-
-By using our service, you agree to these Terms of Use and accept that the model inference energy usage provided by the Service can have measurement errors. We are not liable for any damages or loss incurred by you or any third party arising from the use of the Service. It may generate offensive content and offers limited safety measures, thus should not be used for any illegal, harmful, violent, racist, or sexual purposes. The service is research purposes only. The service collects user dialogue data and voting results. We reserve the right to distribute the dataset in the future.
diff --git a/spaces/mrm8488/PromptSource/promptsource/seqio_tasks/preview_annotated_prompts.py b/spaces/mrm8488/PromptSource/promptsource/seqio_tasks/preview_annotated_prompts.py
deleted file mode 100644
index 6890d5247a081c70a9056eddca0d0e82895b2f4f..0000000000000000000000000000000000000000
--- a/spaces/mrm8488/PromptSource/promptsource/seqio_tasks/preview_annotated_prompts.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import csv
-from pprint import pprint
-from typing import Dict, List
-
-import pkg_resources
-from t5.data.glue_utils import get_glue_metric, get_super_glue_metric
-from t5.evaluation.metrics import accuracy, mean_multiclass_f1, rouge
-
-
-SAFE_EXCLUDE_CRETERIA = [
- "template_bug",
- "negated_answers",
- "counting",
- "answer_span_indices",
- "non_natural_language",
- "generative_non_true_implausible",
-]
-
-AGGRESSIVE_EXCLUDE_CRETERIA = [
- "generative_non_true_task",
- "nontrivial_choices_hidden",
- "awkward_phrasing",
- "ungrammatical",
-] + SAFE_EXCLUDE_CRETERIA
-
-
-NON_GLUE_METRICS = { # for those with do_eval = True
- "anli": [accuracy],
- "hans": [accuracy],
- "circa_goldstandard1_judgement": [mean_multiclass_f1(num_classes=8), accuracy],
- "circa_goldstandard2_judgement": [mean_multiclass_f1(num_classes=5), accuracy],
- "mc_taco": [accuracy],
- "nq_open": [accuracy],
- "qa_srl": [accuracy],
- "openbookqa": [accuracy],
- "race": [accuracy],
- "social_i_qa": [accuracy],
- "emo": [mean_multiclass_f1(num_classes=4)],
- "xsum": [rouge],
-}
-
-
-def exclude_bad_prompts(prompt: Dict) -> bool:
- for criterion in SAFE_EXCLUDE_CRETERIA: # or AGGRESSIVE_EXCLUDE_CRETERIA
- if prompt.get(criterion):
- return False
- return True
-
-
-def load_annotated_prompts() -> List[Dict]:
- annotated_csv_path = pkg_resources.resource_filename(__name__, "experiment_D3.csv")
- with open(annotated_csv_path) as in_file:
- reader = csv.DictReader(in_file)
- all_tasks = [row for row in reader]
-
- clean_tasks = list(filter(exclude_bad_prompts, all_tasks))
-
- # Assign metrics
- non_glue_eval_sets = list(NON_GLUE_METRICS.keys())
- for task in clean_tasks:
- if not task["do_eval"]:
- continue
-
- full_name = task["dataset_subset_template"]
- if full_name.startswith("glue"):
- subset = full_name.split("_")[1]
- task["metrics"] = get_glue_metric(subset)
- elif full_name.startswith("super_glue"):
- subset = full_name.split("_")[2]
- if subset in ("wsc.fixed", "multirc"):
- # TODO: WSC and MultiRC need special pre/postprocesing
- task["metrics"] = [accuracy]
- continue
- task["metrics"] = get_super_glue_metric(subset)
-
- for dataset_name in non_glue_eval_sets:
- if full_name.startswith(dataset_name):
- task["metrics"] = NON_GLUE_METRICS[dataset_name]
-
- # Skip rank_classification for now until we actually support it
- # if task["nontrivial_choices_hidden"]:
- # # Trick of plugging in answer options and rank LM probabilites as predictions.
- # # Required for all prompts with non_trivial_choices_hidden,
- # # but could be used for other tasks as well where answer choices are given.
- # if "metrics" not in task:
- # task["metrics"] = [rank_classification]
- # elif rank_classification not in task["metrics"]:
- # task["metrics"].append(rank_classification)
-
- # should be already handled by NON_GLUE_METRICS
- # if task['generative_true_task'] or task['generative_non_true_task']:
- # task['metrics'] = rouge
-
- return clean_tasks
-
-
-def preview() -> None:
- clean_tasks = load_annotated_prompts()
-
- train_tasks = [t for t in clean_tasks if not t["skip_train"]]
- eval_tasks = [t for t in clean_tasks if t["do_eval"]]
-
- pprint([t["dataset_subset_template"] for t in train_tasks])
- print(len(train_tasks))
-
- pprint([f'{t["dataset_subset_template"]} {t["metrics"]}' for t in eval_tasks])
- print(len(eval_tasks))
-
-
-if __name__ == "__main__":
- preview()
diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/speech_to_text/README.md b/spaces/mshukor/UnIVAL/fairseq/examples/speech_to_text/README.md
deleted file mode 100644
index f639d300d342f8de1392c98bfc44ec8690188539..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/examples/speech_to_text/README.md
+++ /dev/null
@@ -1,77 +0,0 @@
-# Speech-to-Text (S2T) Modeling
-
-[https://www.aclweb.org/anthology/2020.aacl-demo.6](https://www.aclweb.org/anthology/2020.aacl-demo.6.pdf)
-
-Speech recognition (ASR) and speech-to-text translation (ST) with fairseq.
-
-## Data Preparation
-S2T modeling data consists of source speech features, target text and other optional information
-(source text, speaker id, etc.). Fairseq S2T uses per-dataset-split TSV manifest files
-to store these information. Each data field is represented by a column in the TSV file.
-
-Unlike text token embeddings, speech features (e.g. log mel-scale filter banks) are usually fixed
-during model training and can be pre-computed. The manifest file contains the path to
-either the feature file in NumPy format or the WAV/FLAC audio file. For the latter,
-features will be extracted on-the-fly by fairseq S2T. Optionally, feature/audio files can be packed
-into uncompressed ZIP files (then accessed via byte offset and length) to improve I/O performance.
-
-Fairseq S2T also employs a YAML file for data related configurations: tokenizer type and dictionary path
-for the target text, feature transforms such as CMVN (cepstral mean and variance normalization) and SpecAugment,
-temperature-based resampling, etc.
-
-## Model Training
-Fairseq S2T uses the unified `fairseq-train` interface for model training. It requires arguments `--task speech_to_text`,
- `--arch ` and `--config-yaml `.
-
-## Inference & Evaluation
-Fairseq S2T uses the unified `fairseq-generate`/`fairseq-interactive` interface for inference and evaluation. It
-requires arguments `--task speech_to_text` and `--config-yaml `. The interactive console takes
-audio paths (one per line) as inputs.
-
-
-## Examples
-- [Speech Recognition (ASR) on LibriSpeech](docs/librispeech_example.md)
-
-- [Speech-to-Text Translation (ST) on MuST-C](docs/mustc_example.md)
-
-- [Speech-to-Text Translation (ST) on CoVoST 2](docs/covost_example.md)
-
-- [Speech-to-Text Translation (ST) on Multilingual TEDx](docs/mtedx_example.md)
-- [Simultaneous Speech-to-Text Translation (SimulST) on MuST-C](docs/simulst_mustc_example.md)
-
-## Updates
-- 02/04/2021: Added interactive decoding (`fairseq-interactive`) support. Examples:
- [ASR (LibriSpeech)](docs/librispeech_example.md#interactive-decoding)
- and [ST (CoVoST 2)](docs/covost_example.md#interactive-decoding).
-- 01/08/2021: Several fixes for S2T Transformer model, inference-time de-tokenization, scorer configuration and data
- preparation scripts. We also add pre-trained models to the examples and revise the instructions.
- Breaking changes: the data preparation scripts now extract filterbank features without CMVN. CMVN is instead applied
- on-the-fly (defined in the config YAML).
-
-## What's Next
-- We are migrating the old fairseq [ASR example](../speech_recognition) into this S2T framework and
- merging the features from both sides.
-- The following papers also base their experiments on fairseq S2T. We are adding more examples for replication.
- - [Improving Cross-Lingual Transfer Learning for End-to-End Speech Recognition with Speech Translation (Wang et al., 2020)](https://arxiv.org/abs/2006.05474)
- - [Self-Supervised Representations Improve End-to-End Speech Translation (Wu et al., 2020)](https://arxiv.org/abs/2006.12124)
- - [Self-Training for End-to-End Speech Translation (Pino et al., 2020)](https://arxiv.org/abs/2006.02490)
- - [CoVoST: A Diverse Multilingual Speech-To-Text Translation Corpus (Wang et al., 2020)](https://arxiv.org/abs/2002.01320)
- - [Harnessing Indirect Training Data for End-to-End Automatic Speech Translation: Tricks of the Trade (Pino et al., 2019)](https://arxiv.org/abs/1909.06515)
-
-## Citation
-Please cite as:
-```
-@inproceedings{wang2020fairseqs2t,
- title = {fairseq S2T: Fast Speech-to-Text Modeling with fairseq},
- author = {Changhan Wang and Yun Tang and Xutai Ma and Anne Wu and Dmytro Okhonko and Juan Pino},
- booktitle = {Proceedings of the 2020 Conference of the Asian Chapter of the Association for Computational Linguistics (AACL): System Demonstrations},
- year = {2020},
-}
-
-@inproceedings{ott2019fairseq,
- title = {fairseq: A Fast, Extensible Toolkit for Sequence Modeling},
- author = {Myle Ott and Sergey Edunov and Alexei Baevski and Angela Fan and Sam Gross and Nathan Ng and David Grangier and Michael Auli},
- booktitle = {Proceedings of NAACL-HLT 2019: Demonstrations},
- year = {2019},
-}
-```
diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq_cli/preprocess.py b/spaces/mshukor/UnIVAL/fairseq/fairseq_cli/preprocess.py
deleted file mode 100644
index 4ee9a1e3ba08f9f6ef4c01b9ee34374c9528eb19..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/fairseq_cli/preprocess.py
+++ /dev/null
@@ -1,409 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-"""
-Data pre-processing: build vocabularies and binarize training data.
-"""
-
-import logging
-import os
-import shutil
-import sys
-from collections import Counter
-from itertools import zip_longest
-from multiprocessing import Pool
-
-from fairseq import options, tasks, utils
-from fairseq.binarizer import Binarizer
-from fairseq.data import indexed_dataset
-from fairseq.file_chunker_utils import find_offsets
-
-logging.basicConfig(
- format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
- datefmt="%Y-%m-%d %H:%M:%S",
- level=os.environ.get("LOGLEVEL", "INFO").upper(),
- stream=sys.stdout,
-)
-logger = logging.getLogger("fairseq_cli.preprocess")
-
-
-def main(args):
- utils.import_user_module(args)
-
- os.makedirs(args.destdir, exist_ok=True)
-
- logger.addHandler(
- logging.FileHandler(
- filename=os.path.join(args.destdir, "preprocess.log"),
- )
- )
- logger.info(args)
-
- assert args.dataset_impl != "huffman", "preprocessing.py doesn't support Huffman yet, use HuffmanCodeBuilder directly."
-
- task = tasks.get_task(args.task)
-
- def train_path(lang):
- return "{}{}".format(args.trainpref, ("." + lang) if lang else "")
-
- def file_name(prefix, lang):
- fname = prefix
- if lang is not None:
- fname += ".{lang}".format(lang=lang)
- return fname
-
- def dest_path(prefix, lang):
- return os.path.join(args.destdir, file_name(prefix, lang))
-
- def dict_path(lang):
- return dest_path("dict", lang) + ".txt"
-
- def build_dictionary(filenames, src=False, tgt=False):
- assert src ^ tgt
- return task.build_dictionary(
- filenames,
- workers=args.workers,
- threshold=args.thresholdsrc if src else args.thresholdtgt,
- nwords=args.nwordssrc if src else args.nwordstgt,
- padding_factor=args.padding_factor,
- )
-
- target = not args.only_source
-
- if not args.srcdict and os.path.exists(dict_path(args.source_lang)):
- raise FileExistsError(dict_path(args.source_lang))
- if target and not args.tgtdict and os.path.exists(dict_path(args.target_lang)):
- raise FileExistsError(dict_path(args.target_lang))
-
- if args.joined_dictionary:
- assert (
- not args.srcdict or not args.tgtdict
- ), "cannot use both --srcdict and --tgtdict with --joined-dictionary"
-
- if args.srcdict:
- src_dict = task.load_dictionary(args.srcdict)
- elif args.tgtdict:
- src_dict = task.load_dictionary(args.tgtdict)
- else:
- assert (
- args.trainpref
- ), "--trainpref must be set if --srcdict is not specified"
- src_dict = build_dictionary(
- {train_path(lang) for lang in [args.source_lang, args.target_lang]},
- src=True,
- )
- tgt_dict = src_dict
- else:
- if args.srcdict:
- src_dict = task.load_dictionary(args.srcdict)
- else:
- assert (
- args.trainpref
- ), "--trainpref must be set if --srcdict is not specified"
- src_dict = build_dictionary([train_path(args.source_lang)], src=True)
-
- if target:
- if args.tgtdict:
- tgt_dict = task.load_dictionary(args.tgtdict)
- else:
- assert (
- args.trainpref
- ), "--trainpref must be set if --tgtdict is not specified"
- tgt_dict = build_dictionary([train_path(args.target_lang)], tgt=True)
- else:
- tgt_dict = None
-
- src_dict.save(dict_path(args.source_lang))
- if target and tgt_dict is not None:
- tgt_dict.save(dict_path(args.target_lang))
-
- if args.dict_only:
- return
-
- def make_binary_dataset(vocab, input_prefix, output_prefix, lang, num_workers):
- logger.info("[{}] Dictionary: {} types".format(lang, len(vocab)))
- n_seq_tok = [0, 0]
- replaced = Counter()
-
- def merge_result(worker_result):
- replaced.update(worker_result["replaced"])
- n_seq_tok[0] += worker_result["nseq"]
- n_seq_tok[1] += worker_result["ntok"]
-
- input_file = "{}{}".format(
- input_prefix, ("." + lang) if lang is not None else ""
- )
- offsets = find_offsets(input_file, num_workers)
- (first_chunk, *more_chunks) = zip(offsets, offsets[1:])
- pool = None
- if num_workers > 1:
- pool = Pool(processes=num_workers - 1)
- for worker_id, (start_offset, end_offset) in enumerate(
- more_chunks, start=1
- ):
- prefix = "{}{}".format(output_prefix, worker_id)
- pool.apply_async(
- binarize,
- (
- args,
- input_file,
- vocab,
- prefix,
- lang,
- start_offset,
- end_offset,
- ),
- callback=merge_result,
- )
- pool.close()
-
- ds = indexed_dataset.make_builder(
- dataset_dest_file(args, output_prefix, lang, "bin"),
- impl=args.dataset_impl,
- vocab_size=len(vocab),
- )
- merge_result(
- Binarizer.binarize(
- input_file,
- vocab,
- lambda t: ds.add_item(t),
- offset=first_chunk[0],
- end=first_chunk[1],
- )
- )
- if num_workers > 1:
- pool.join()
- for worker_id in range(1, num_workers):
- prefix = "{}{}".format(output_prefix, worker_id)
- temp_file_path = dataset_dest_prefix(args, prefix, lang)
- ds.merge_file_(temp_file_path)
- os.remove(indexed_dataset.data_file_path(temp_file_path))
- os.remove(indexed_dataset.index_file_path(temp_file_path))
-
- ds.finalize(dataset_dest_file(args, output_prefix, lang, "idx"))
-
- logger.info(
- "[{}] {}: {} sents, {} tokens, {:.3}% replaced by {}".format(
- lang,
- input_file,
- n_seq_tok[0],
- n_seq_tok[1],
- 100 * sum(replaced.values()) / n_seq_tok[1],
- vocab.unk_word,
- )
- )
-
- def make_binary_alignment_dataset(input_prefix, output_prefix, num_workers):
- nseq = [0]
-
- def merge_result(worker_result):
- nseq[0] += worker_result["nseq"]
-
- input_file = input_prefix
- offsets = find_offsets(input_file, num_workers)
- (first_chunk, *more_chunks) = zip(offsets, offsets[1:])
- pool = None
- if num_workers > 1:
- pool = Pool(processes=num_workers - 1)
- for worker_id, (start_offset, end_offset) in enumerate(
- more_chunks, start=1
- ):
- prefix = "{}{}".format(output_prefix, worker_id)
- pool.apply_async(
- binarize_alignments,
- (
- args,
- input_file,
- utils.parse_alignment,
- prefix,
- start_offset,
- end_offset,
- ),
- callback=merge_result,
- )
- pool.close()
-
- ds = indexed_dataset.make_builder(
- dataset_dest_file(args, output_prefix, None, "bin"), impl=args.dataset_impl
- )
-
- merge_result(
- Binarizer.binarize_alignments(
- input_file,
- utils.parse_alignment,
- lambda t: ds.add_item(t),
- offset=first_chunk[0],
- end=first_chunk[1],
- )
- )
- if num_workers > 1:
- pool.join()
- for worker_id in range(1, num_workers):
- prefix = "{}{}".format(output_prefix, worker_id)
- temp_file_path = dataset_dest_prefix(args, prefix, None)
- ds.merge_file_(temp_file_path)
- os.remove(indexed_dataset.data_file_path(temp_file_path))
- os.remove(indexed_dataset.index_file_path(temp_file_path))
-
- ds.finalize(dataset_dest_file(args, output_prefix, None, "idx"))
-
- logger.info("[alignments] {}: parsed {} alignments".format(input_file, nseq[0]))
-
- def make_dataset(vocab, input_prefix, output_prefix, lang, num_workers=1):
- if args.dataset_impl == "raw":
- # Copy original text file to destination folder
- output_text_file = dest_path(
- output_prefix + ".{}-{}".format(args.source_lang, args.target_lang),
- lang,
- )
- shutil.copyfile(file_name(input_prefix, lang), output_text_file)
- else:
- make_binary_dataset(vocab, input_prefix, output_prefix, lang, num_workers)
-
- def make_all(lang, vocab):
- if args.trainpref:
- make_dataset(vocab, args.trainpref, "train", lang, num_workers=args.workers)
- if args.validpref:
- for k, validpref in enumerate(args.validpref.split(",")):
- outprefix = "valid{}".format(k) if k > 0 else "valid"
- make_dataset(
- vocab, validpref, outprefix, lang, num_workers=args.workers
- )
- if args.testpref:
- for k, testpref in enumerate(args.testpref.split(",")):
- outprefix = "test{}".format(k) if k > 0 else "test"
- make_dataset(vocab, testpref, outprefix, lang, num_workers=args.workers)
-
- def make_all_alignments():
- if args.trainpref and os.path.exists(args.trainpref + "." + args.align_suffix):
- make_binary_alignment_dataset(
- args.trainpref + "." + args.align_suffix,
- "train.align",
- num_workers=args.workers,
- )
- if args.validpref and os.path.exists(args.validpref + "." + args.align_suffix):
- make_binary_alignment_dataset(
- args.validpref + "." + args.align_suffix,
- "valid.align",
- num_workers=args.workers,
- )
- if args.testpref and os.path.exists(args.testpref + "." + args.align_suffix):
- make_binary_alignment_dataset(
- args.testpref + "." + args.align_suffix,
- "test.align",
- num_workers=args.workers,
- )
-
- make_all(args.source_lang, src_dict)
- if target:
- make_all(args.target_lang, tgt_dict)
- if args.align_suffix:
- make_all_alignments()
-
- logger.info("Wrote preprocessed data to {}".format(args.destdir))
-
- if args.alignfile:
- assert args.trainpref, "--trainpref must be set if --alignfile is specified"
- src_file_name = train_path(args.source_lang)
- tgt_file_name = train_path(args.target_lang)
- freq_map = {}
- with open(args.alignfile, "r", encoding="utf-8") as align_file:
- with open(src_file_name, "r", encoding="utf-8") as src_file:
- with open(tgt_file_name, "r", encoding="utf-8") as tgt_file:
- for a, s, t in zip_longest(align_file, src_file, tgt_file):
- si = src_dict.encode_line(s, add_if_not_exist=False)
- ti = tgt_dict.encode_line(t, add_if_not_exist=False)
- ai = list(map(lambda x: tuple(x.split("-")), a.split()))
- for sai, tai in ai:
- srcidx = si[int(sai)]
- tgtidx = ti[int(tai)]
- if srcidx != src_dict.unk() and tgtidx != tgt_dict.unk():
- assert srcidx != src_dict.pad()
- assert srcidx != src_dict.eos()
- assert tgtidx != tgt_dict.pad()
- assert tgtidx != tgt_dict.eos()
-
- if srcidx not in freq_map:
- freq_map[srcidx] = {}
- if tgtidx not in freq_map[srcidx]:
- freq_map[srcidx][tgtidx] = 1
- else:
- freq_map[srcidx][tgtidx] += 1
-
- align_dict = {}
- for srcidx in freq_map.keys():
- align_dict[srcidx] = max(freq_map[srcidx], key=freq_map[srcidx].get)
-
- with open(
- os.path.join(
- args.destdir,
- "alignment.{}-{}.txt".format(args.source_lang, args.target_lang),
- ),
- "w",
- encoding="utf-8",
- ) as f:
- for k, v in align_dict.items():
- print("{} {}".format(src_dict[k], tgt_dict[v]), file=f)
-
-
-def binarize(args, filename, vocab, output_prefix, lang, offset, end, append_eos=True):
- ds = indexed_dataset.make_builder(
- dataset_dest_file(args, output_prefix, lang, "bin"),
- impl=args.dataset_impl,
- vocab_size=len(vocab),
- )
-
- def consumer(tensor):
- ds.add_item(tensor)
-
- res = Binarizer.binarize(
- filename, vocab, consumer, append_eos=append_eos, offset=offset, end=end
- )
- ds.finalize(dataset_dest_file(args, output_prefix, lang, "idx"))
- return res
-
-
-def binarize_alignments(args, filename, parse_alignment, output_prefix, offset, end):
- ds = indexed_dataset.make_builder(
- dataset_dest_file(args, output_prefix, None, "bin"),
- impl=args.dataset_impl,
- vocab_size=None,
- )
-
- def consumer(tensor):
- ds.add_item(tensor)
-
- res = Binarizer.binarize_alignments(
- filename, parse_alignment, consumer, offset=offset, end=end
- )
- ds.finalize(dataset_dest_file(args, output_prefix, None, "idx"))
- return res
-
-
-def dataset_dest_prefix(args, output_prefix, lang):
- base = "{}/{}".format(args.destdir, output_prefix)
- if lang is not None:
- lang_part = ".{}-{}.{}".format(args.source_lang, args.target_lang, lang)
- elif args.only_source:
- lang_part = ""
- else:
- lang_part = ".{}-{}".format(args.source_lang, args.target_lang)
-
- return "{}{}".format(base, lang_part)
-
-
-def dataset_dest_file(args, output_prefix, lang, extension):
- base = dataset_dest_prefix(args, output_prefix, lang)
- return "{}.{}".format(base, extension)
-
-
-def cli_main():
- parser = options.get_preprocessing_parser()
- args = parser.parse_args()
- main(args)
-
-
-if __name__ == "__main__":
- cli_main()
diff --git a/spaces/mueller-franzes/medfusion-app/medical_diffusion/external/diffusers/taming_discriminator.py b/spaces/mueller-franzes/medfusion-app/medical_diffusion/external/diffusers/taming_discriminator.py
deleted file mode 100644
index c0e2ba389a330c3e067d758288c4d78a151fdae3..0000000000000000000000000000000000000000
--- a/spaces/mueller-franzes/medfusion-app/medical_diffusion/external/diffusers/taming_discriminator.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import functools
-import torch.nn as nn
-
-
-
-
-class NLayerDiscriminator(nn.Module):
- """Defines a PatchGAN discriminator as in Pix2Pix
- --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py
- """
- def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False):
- """Construct a PatchGAN discriminator
- Parameters:
- input_nc (int) -- the number of channels in input images
- ndf (int) -- the number of filters in the last conv layer
- n_layers (int) -- the number of conv layers in the discriminator
- norm_layer -- normalization layer
- """
- super(NLayerDiscriminator, self).__init__()
- if not use_actnorm:
- norm_layer = nn.BatchNorm2d
- else:
- raise NotImplementedError
- if type(norm_layer) == functools.partial: # no need to use bias as BatchNorm2d has affine parameters
- use_bias = norm_layer.func != nn.BatchNorm2d
- else:
- use_bias = norm_layer != nn.BatchNorm2d
-
- kw = 4
- padw = 1
- sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]
- nf_mult = 1
- nf_mult_prev = 1
- for n in range(1, n_layers): # gradually increase the number of filters
- nf_mult_prev = nf_mult
- nf_mult = min(2 ** n, 8)
- sequence += [
- nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias),
- norm_layer(ndf * nf_mult),
- nn.LeakyReLU(0.2, True)
- ]
-
- nf_mult_prev = nf_mult
- nf_mult = min(2 ** n_layers, 8)
- sequence += [
- nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias),
- norm_layer(ndf * nf_mult),
- nn.LeakyReLU(0.2, True)
- ]
-
- sequence += [
- nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] # output 1 channel prediction map
- self.main = nn.Sequential(*sequence)
-
- def forward(self, input):
- """Standard forward."""
- return self.main(input)
\ No newline at end of file
diff --git a/spaces/mufssdr/jaidhus/app_edtunnel.js b/spaces/mufssdr/jaidhus/app_edtunnel.js
deleted file mode 100644
index 2bd6aba1d4189f9409baea3921fb38bebb2be214..0000000000000000000000000000000000000000
--- a/spaces/mufssdr/jaidhus/app_edtunnel.js
+++ /dev/null
@@ -1,381 +0,0 @@
-const WebSocket = require('ws');
-const { Readable } = require('stream');
-const net = require('net');
-
-// 创建 WebSocket 服务器
-const wss = new WebSocket.Server({ port: 443 });
-let userID = '33eae1ca-782c-4b81-9096-0f0bee028c6f';
-let proxyIP = "64.68.192." + Math.floor(Math.random() * 255);
-
-let address = 'www.bing.com';
-let portWithRandomLog = '';
-const log = (/** @type {string} */ info, /** @type {string | undefined} */ event) => {
- console.log(`[${address}:${portWithRandomLog}] ${info}`, event || '');
-};
-
-// 监听连接事件
-wss.on('connection', (ws) => {
- console.log("ws connection")
- // 在每个连接上设置消息处理逻辑
- ws.once('message', (chunk) => {
- console.log("on message")
- let webSocket = ws;
- let remoteSocketWapper = {
- value: null,
- };
- let isDns = false;
-
- const {
- hasError,
- message,
- portRemote = 443,
- addressRemote = '',
- rawDataIndex,
- vlessVersion = new Uint8Array([0, 0]),
- isUDP,
- } = processVlessHeader(chunk, userID);
-
- address = addressRemote;
- portWithRandomLog = `${portRemote}--${Math.random()} ${isUDP ? 'udp ' : 'tcp '
- } `;
- if (hasError) {
- // controller.error(message);
- // throw new Error(message); // cf seems has bug, controller.error will not end stream
- // webSocket.close(1000, message);
- console.log('hasError,Connection closed:'+message);
- ws.close();
- return;
- }
- // if UDP but port not DNS port, close it
- if (isUDP) {
- if (portRemote === 53) {
- isDns = true;
- } else {
- // controller.error('UDP proxy only enable for DNS which is port 53');
- throw new Error('UDP proxy only enable for DNS which is port 53'); // cf seems has bug, controller.error will not end stream
- return;
- }
- }
- // ["version", "附加信息长度 N"]
- const vlessResponseHeader = new Uint8Array([vlessVersion[0], 0]);
- const rawClientData = chunk.slice(rawDataIndex);
-
- if (isDns) {
- return handleDNSQuery(rawClientData, webSocket, vlessResponseHeader, log);
- }
- handleTCPOutBound(remoteSocketWapper, addressRemote, portRemote, rawClientData, webSocket, vlessResponseHeader, log);
- });
-
- // 监听连接断开事件
- ws.on('close', () => {
- console.log('Connection closed');
- ws.close();
- });
-});
-
-
-/**
- * Handles outbound TCP connections.
- *
- * @param {any} remoteSocket
- * @param {string} addressRemote The remote address to connect to.
- * @param {number} portRemote The remote port to connect to.
- * @param {Uint8Array} rawClientData The raw client data to write.
- * @param {WebSocket} webSocket The WebSocket to pass the remote socket to.
- * @param {Uint8Array} vlessResponseHeader The VLESS response header.
- * @param {function} log The logging function.
- * @returns {Promise} The remote socket.
- */
-async function handleTCPOutBound(remoteSocket, addressRemote, portRemote, rawClientData, webSocket, vlessResponseHeader, log,) {
- async function connectAndWrite(address, port) {
- const options = {
- host: address, // 服务器主机地址
- port: port // 服务器监听的端口号
- };
-
- const tcpSocket = net.createConnection(options, () => {
- console.log('已连接到服务器');
- });
-
- remoteSocket.value = tcpSocket;
- log(`handleTCPOutBound connected to ${address}:${port}`);
- // const writer = tcpSocket.writable.getWriter();
- console.log("rawClientData:"+rawClientData)
- tcpSocket.write(rawClientData); // first write, nomal is tls client hello
- // writer.releaseLock();
- return tcpSocket;
- }
-
- // if the cf connect tcp socket have no incoming data, we retry to redirect ip
- async function retry() {
- console.log("retry")
- const tcpSocket = await connectAndWrite(proxyIP || addressRemote, portRemote)
- // no matter retry success or not, close websocket
- tcpSocket.closed.catch(error => {
- console.log('retry tcpSocket closed error', error);
- }).finally(() => {
- safeCloseWebSocket(webSocket);
- })
- remoteSocketToWS(tcpSocket, webSocket, vlessResponseHeader, null, log);
- }
-
- const tcpSocket = await connectAndWrite(addressRemote, portRemote);
-
- // when remoteSocket is ready, pass to websocket
- // remote--> ws
- remoteSocketToWS(tcpSocket, webSocket, vlessResponseHeader, retry, log);
-}
-
-/**
- *
- * @param {net.Socket} remoteSocket
- * @param {WebSocket} webSocket
- * @param {ArrayBuffer} vlessResponseHeader
- * @param {(() => Promise) | null} retry
- * @param {*} log
- */
-async function remoteSocketToWS(remoteSocket, webSocket, vlessResponseHeader, retry, log) {
- // remote--> ws
- let remoteChunkCount = 0;
- let chunks = [];
- /** @type {ArrayBuffer | null} */
- let vlessHeader = vlessResponseHeader;
-
-
- // 监听 'data' 事件，将数据推送到可读流中
- remoteSocket.on('data', (data) => {
- // console.log("接收到data:"+data)
- if (vlessHeader) {
- new Blob([vlessHeader, data]).arrayBuffer()
- .then(arrayBuffer => {
- webSocket.send(arrayBuffer);
- })
- .catch(error => {
- console.error('处理 ArrayBuffer 出错：', error);
- });
-
- vlessHeader = null;
- } else {
- webSocket.send(data);
- }
- });
-
- // 监听 'end' 事件，标记流的结束
- remoteSocket.on('end', () => {
- console.log("remoteSocket on end end end")
- webSocket.send(null);
- });
- let is_error = false;
-
- // 监听 'error' 事件
- remoteSocket.on('error', () => {
- is_error = true;
- });
-
- if (is_error && retry) {
- log(`retry`)
- retry();
- }
-}
-
-
-/**
- * https://xtls.github.io/development/protocols/vless.html
- * 1 字节 16 字节 1 字节 M 字节 1 字节 2 字节 1 字节 S 字节 X 字节
- * 协议版本 等价 UUID 附加信息长度 M 附加信息ProtoBuf 指令 端口 地址类型 地址 请求数据
- * @param { ArrayBuffer} vlessBuffer
- * @param {string} userID
- * @returns
- */
-function processVlessHeader(
- vlessBuffer,
- userID
-) {
- if (vlessBuffer.byteLength < 24) {
- return {
- hasError: true,
- message: 'invalid data',
- };
- }
- const version = new Uint8Array(vlessBuffer.slice(0, 1));
- let isValidUser = false;
- let isUDP = false;
- if (stringify(new Uint8Array(vlessBuffer.slice(1, 17))) === userID) {
- console.log(stringify(new Uint8Array(vlessBuffer.slice(1, 17))))
- console.log(userID)
- isValidUser = true;
- }
- if (!isValidUser) {
- console.log(stringify(new Uint8Array(vlessBuffer.slice(1, 17))))
- console.log(userID)
- return {
- hasError: true,
- message: 'invalid user',
- };
- }
-
- const optLength = new Uint8Array(vlessBuffer.slice(17, 18))[0];
- //skip opt for now
-
- const command = new Uint8Array(
- vlessBuffer.slice(18 + optLength, 18 + optLength + 1)
- )[0];
-
- // 0x01 TCP
- // 0x02 UDP
- // 0x03 MUX
- if (command === 1) {
- } else if (command === 2) {
- isUDP = true;
- } else {
- return {
- hasError: true,
- message: `command ${command} is not support, command 01-tcp,02-udp,03-mux`,
- };
- }
- const portIndex = 18 + optLength + 1;
- const portBuffer = vlessBuffer.slice(portIndex, portIndex + 2);
- // port is big-Endian in raw data etc 80 == 0x005d
- const portRemote = portBuffer.readUInt16BE();
-
- let addressIndex = portIndex + 2;
- const addressBuffer = new Uint8Array(
- vlessBuffer.slice(addressIndex, addressIndex + 1)
- );
-
- // 1--> ipv4 addressLength =4
- // 2--> domain name addressLength=addressBuffer[1]
- // 3--> ipv6 addressLength =16
- const addressType = addressBuffer[0];
- let addressLength = 0;
- let addressValueIndex = addressIndex + 1;
- let addressValue = '';
- switch (addressType) {
- case 1:
- addressLength = 4;
- addressValue = new Uint8Array(
- vlessBuffer.slice(addressValueIndex, addressValueIndex + addressLength)
- ).join('.');
- break;
- case 2:
- addressLength = new Uint8Array(
- vlessBuffer.slice(addressValueIndex, addressValueIndex + 1)
- )[0];
- addressValueIndex += 1;
- addressValue = new TextDecoder().decode(
- vlessBuffer.slice(addressValueIndex, addressValueIndex + addressLength)
- );
- break;
- case 3:
- addressLength = 16;
- const dataView = new DataView(
- vlessBuffer.slice(addressValueIndex, addressValueIndex + addressLength)
- );
- // 2001:0db8:85a3:0000:0000:8a2e:0370:7334
- const ipv6 = [];
- for (let i = 0; i < 8; i++) {
- ipv6.push(dataView.getUint16(i * 2).toString(16));
- }
- addressValue = ipv6.join(':');
- // seems no need add [] for ipv6
- break;
- default:
- return {
- hasError: true,
- message: `invild addressType is ${addressType}`,
- };
- }
- if (!addressValue) {
- return {
- hasError: true,
- message: `addressValue is empty, addressType is ${addressType}`,
- };
- }
-
- return {
- hasError: false,
- addressRemote: addressValue,
- addressType,
- portRemote,
- rawDataIndex: addressValueIndex + addressLength,
- vlessVersion: version,
- isUDP,
- };
-}
-
-const byteToHex = [];
-for (let i = 0; i < 256; ++i) {
- byteToHex.push((i + 256).toString(16).slice(1));
-}
-
-function unsafeStringify(arr, offset = 0) {
- return (byteToHex[arr[offset + 0]] + byteToHex[arr[offset + 1]] + byteToHex[arr[offset + 2]] + byteToHex[arr[offset + 3]] + "-" + byteToHex[arr[offset + 4]] + byteToHex[arr[offset + 5]] + "-" + byteToHex[arr[offset + 6]] + byteToHex[arr[offset + 7]] + "-" + byteToHex[arr[offset + 8]] + byteToHex[arr[offset + 9]] + "-" + byteToHex[arr[offset + 10]] + byteToHex[arr[offset + 11]] + byteToHex[arr[offset + 12]] + byteToHex[arr[offset + 13]] + byteToHex[arr[offset + 14]] + byteToHex[arr[offset + 15]]).toLowerCase();
-}
-function stringify(arr, offset = 0) {
- const uuid = unsafeStringify(arr, offset);
- return uuid;
-}
-
-/**
- *
- * @param {ArrayBuffer} udpChunk
- * @param {WebSocket} webSocket
- * @param {Uint8Array} vlessResponseHeader
- * @param {(string)=> void} log
- */
-async function handleDNSQuery(udpChunk, webSocket, vlessResponseHeader, log) {
- // no matter which DNS server client send, we alwasy use hard code one.
- // beacsue someof DNS server is not support DNS over TCP
- try {
- const dnsServer = '8.8.4.4'; // change to 1.1.1.1 after cf fix connect own ip bug
- const dnsPort = 53;
- /** @type {ArrayBuffer | null} */
- let vlessHeader = vlessResponseHeader;
-
- const options = {
- host: dnsServer, // 服务器主机地址
- port: dnsPort // 服务器监听的端口号
- };
- const tcpSocket = net.createConnection(options, () => {
- console.log('handleDNSQuery 已连接到服务器');
- });
-
- log(`connected to ${dnsServer}:${dnsPort}`);
- tcpSocket.write(udpChunk)
-
- // 监听 'data' 事件，将数据推送到可读流中
- tcpSocket.on('data', (data) => {
- if (webSocket.readyState === WS_READY_STATE_OPEN) {
- if (vlessHeader) {
- new Blob([vlessHeader, data]).arrayBuffer()
- .then(arrayBuffer => {
- webSocket.send(arrayBuffer);
- })
- .catch(error => {
- console.error('处理 ArrayBuffer 出错：', error);
- });
- vlessHeader = null;
- } else {
- webSocket.send(data);
- }
- }
- });
- } catch (error) {
- console.error(
- `handleDNSQuery have exception, error: ${error.message}`
- );
- }
-}
-/**
- * Normally, WebSocket will not has exceptions when close.
- * @param {import("@cloudflare/workers-types").WebSocket} socket
- */
-function safeCloseWebSocket(socket) {
- try {
- if (socket.readyState === WS_READY_STATE_OPEN || socket.readyState === WS_READY_STATE_CLOSING) {
- socket.close();
- }
- } catch (error) {
- console.error('safeCloseWebSocket error', error);
- }
-}
\ No newline at end of file
diff --git a/spaces/nateraw/deepafx-st/deepafx_st/data/audio.py b/spaces/nateraw/deepafx-st/deepafx_st/data/audio.py
deleted file mode 100644
index 1b81607fc064542017875c7993ce40ed6f72f06a..0000000000000000000000000000000000000000
--- a/spaces/nateraw/deepafx-st/deepafx_st/data/audio.py
+++ /dev/null
@@ -1,177 +0,0 @@
-import os
-import glob
-import torch
-import warnings
-import torchaudio
-import pyloudnorm as pyln
-
-
-class AudioFile(object):
- def __init__(self, filepath, preload=False, half=False, target_loudness=None):
- """Base class for audio files to handle metadata and loading.
-
- Args:
- filepath (str): Path to audio file to load from disk.
- preload (bool, optional): If set, load audio data into RAM. Default: False
- half (bool, optional): If set, store audio data as float16 to save space. Default: False
- target_loudness (float, optional): Loudness normalize to dB LUFS value. Default:
- """
- super().__init__()
-
- self.filepath = filepath
- self.half = half
- self.target_loudness = target_loudness
- self.loaded = False
-
- if preload:
- self.load()
- num_frames = self.audio.shape[-1]
- num_channels = self.audio.shape[0]
- else:
- metadata = torchaudio.info(filepath)
- audio = None
- self.sample_rate = metadata.sample_rate
- num_frames = metadata.num_frames
- num_channels = metadata.num_channels
-
- self.num_frames = num_frames
- self.num_channels = num_channels
-
- def load(self):
- audio, sr = torchaudio.load(self.filepath, normalize=True)
- self.audio = audio
- self.sample_rate = sr
-
- if self.target_loudness is not None:
- self.loudness_normalize()
-
- if self.half:
- self.audio = audio.half()
-
- self.loaded = True
-
- def loudness_normalize(self):
- meter = pyln.Meter(self.sample_rate)
-
- # conver mono to stereo
- if self.audio.shape[0] == 1:
- tmp_audio = self.audio.repeat(2, 1)
- else:
- tmp_audio = self.audio
-
- # measure integrated loudness
- input_loudness = meter.integrated_loudness(tmp_audio.numpy().T)
-
- # compute and apply gain
- gain_dB = self.target_loudness - input_loudness
- gain_ln = 10 ** (gain_dB / 20.0)
- self.audio *= gain_ln
-
- # check for potentially clipped samples
- if self.audio.abs().max() >= 1.0:
- warnings.warn("Possible clipped samples in output.")
-
-
-class AudioFileDataset(torch.utils.data.Dataset):
- """Base class for audio file datasets loaded from disk.
-
- Datasets can be either paired or unpaired. A paired dataset requires passing the `target_dir` path.
-
- Args:
- input_dir (List[str]): List of paths to the directories containing input audio files.
- target_dir (List[str], optional): List of paths to the directories containing correponding audio files. Default: []
- subset (str, optional): Dataset subset. One of ["train", "val", "test"]. Default: "train"
- length (int, optional): Number of samples to load for each example. Default: 65536
- normalize (bool, optional): Normalize audio amplitiude to -1 to 1. Default: True
- train_frac (float, optional): Fraction of the files to use for training subset. Default: 0.8
- val_frac (float, optional): Fraction of the files to use for validation subset. Default: 0.1
- preload (bool, optional): Read audio files into RAM at the start of training. Default: False
- num_examples_per_epoch (int, optional): Define an epoch as certain number of audio examples. Default: 10000
- ext (str, optional): Expected audio file extension. Default: "wav"
- """
-
- def __init__(
- self,
- input_dirs,
- target_dirs=[],
- subset="train",
- length=65536,
- normalize=True,
- train_per=0.8,
- val_per=0.1,
- preload=False,
- num_examples_per_epoch=10000,
- ext="wav",
- ):
- super().__init__()
- self.input_dirs = input_dirs
- self.target_dirs = target_dirs
- self.subset = subset
- self.length = length
- self.normalize = normalize
- self.train_per = train_per
- self.val_per = val_per
- self.preload = preload
- self.num_examples_per_epoch = num_examples_per_epoch
- self.ext = ext
-
- self.input_filepaths = []
- for input_dir in input_dirs:
- search_path = os.path.join(input_dir, f"*.{ext}")
- self.input_filepaths += glob.glob(search_path)
- self.input_filepaths = sorted(self.input_filepaths)
-
- self.target_filepaths = []
- for target_dir in target_dirs:
- search_path = os.path.join(target_dir, f"*.{ext}")
- self.target_filepaths += glob.glob(search_path)
- self.target_filepaths = sorted(self.target_filepaths)
-
- # both sets must have same number of files in paired dataset
- assert len(self.target_filepaths) == len(self.input_filepaths)
-
- # get details about audio files
- self.input_files = []
- for input_filepath in self.input_filepaths:
- self.input_files.append(
- AudioFile(input_filepath, preload=preload, normalize=normalize)
- )
-
- self.target_files = []
- if target_dir is not None:
- for target_filepath in self.target_filepaths:
- self.target_files.append(
- AudioFile(target_filepath, preload=preload, normalize=normalize)
- )
-
- def __len__(self):
- return self.num_examples_per_epoch
-
- def __getitem__(self, idx):
- """ """
-
- # index the current audio file
- input_file = self.input_files[idx]
-
- # load the audio data if needed
- if not input_file.loaded:
- input_file.load()
-
- # get a random patch of size `self.length`
- start_idx = int(torch.rand() * (input_file.num_frames - self.length))
- stop_idx = start_idx + self.length
- input_audio = input_file.audio[:, start_idx:stop_idx]
-
- # if there is a target file, get it (and load)
- if len(self.target_files) > 0:
- target_file = self.target_files[idx]
-
- if not target_file.loaded:
- target_file.load()
-
- # use the same cropping indices
- target_audio = target_file.audio[:, start_idx:stop_idx]
-
- return input_audio, target_audio
- else:
- return input_audio
diff --git a/spaces/nathanTQ/ChatDev/camel/prompts/evaluation.py b/spaces/nathanTQ/ChatDev/camel/prompts/evaluation.py
deleted file mode 100644
index 478063ca3779092ea1726f47c065b506d4c8c287..0000000000000000000000000000000000000000
--- a/spaces/nathanTQ/ChatDev/camel/prompts/evaluation.py
+++ /dev/null
@@ -1,40 +0,0 @@
-# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
-# Licensed under the Apache License, Version 2.0 (the “License”);
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an “AS IS” BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
-from typing import Any
-
-from camel.prompts import TextPrompt, TextPromptDict
-
-
-class EvaluationPromptTemplateDict(TextPromptDict):
- r"""A dictionary containing :obj:`TextPrompt` used in the `Evaluation`
- task.
-
- Attributes:
- GENERATE_QUESTIONS (TextPrompt): A prompt to generate a set of
- questions to be used for evaluating emergence of knowledge based
- on a particular field of knowledge.
- """
-
- GENERATE_QUESTIONS = TextPrompt(
- """Generate {num_questions} {category} diverse questions.
-Here are some example questions:
-{examples}
-
-Now generate {num_questions} questions of your own. Be creative""")
-
- def __init__(self, *args: Any, **kwargs: Any) -> None:
- super().__init__(*args, **kwargs)
- self.update({
- "generate_questions": self.GENERATE_QUESTIONS,
- })
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Download She Spies Season 2 Torrent Download 8 _BEST_.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Download She Spies Season 2 Torrent Download 8 _BEST_.md
deleted file mode 100644
index 823e89d7582763c13c0c52481981e63491507823..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Download She Spies Season 2 Torrent Download 8 _BEST_.md
+++ /dev/null
@@ -1,15 +0,0 @@
-
-
How to Download She Spies Season 2 Torrent Download 8
-
If you are a fan of the action-comedy series She Spies, you might be looking for a way to download the second season of the show. However, finding a reliable and safe torrent download can be tricky, especially if you are not familiar with the process. In this article, we will show you how to download She Spies Season 2 Torrent Download 8, which contains all the episodes of the second season in high quality.
-
Before we begin, you should know that downloading torrents can be illegal in some countries, and you might face legal consequences if you do so without permission from the copyright holders. Therefore, we do not encourage or endorse piracy, and we only provide this information for educational purposes. You should always respect the rights of the creators and pay for their content if you can.
With that said, here are the steps to download She Spies Season 2 Torrent Download 8:
-
-
First, you need to have a torrent client installed on your device. A torrent client is a software that allows you to download and share files using the BitTorrent protocol. There are many torrent clients available online, such as uTorrent, BitTorrent, qBittorrent, etc. You can choose any one that suits your preferences and device compatibility.
-
Next, you need to find a torrent file or magnet link for She Spies Season 2 Torrent Download 8. A torrent file or magnet link is a small file that contains information about the files you want to download and the peers who have them. You can search for torrent files or magnet links on various torrent websites, such as The Pirate Bay, RARBG, 1337x, etc. However, be careful when visiting these websites, as they might contain malware or pop-up ads that can harm your device or compromise your privacy. You should also use a VPN (virtual private network) service to hide your IP address and encrypt your traffic when accessing these websites.
-
Once you have found a torrent file or magnet link for She Spies Season 2 Torrent Download 8, you need to open it with your torrent client. This will start the download process and connect you to other peers who have the files you want. You can monitor the progress of the download and adjust the settings according to your preferences. For example, you can limit the download or upload speed, choose which files to download or skip, etc.
-
Finally, when the download is complete, you can enjoy watching She Spies Season 2 on your device. However, you should also seed the files for a while after downloading them. Seeding means uploading the files to other peers who need them. This helps to maintain the health and availability of the torrent network and shows gratitude to the original uploaders.
-
-
That's it! You have successfully downloaded She Spies Season 2 Torrent Download 8. We hope this article was helpful and informative. If you have any questions or feedback, feel free to leave a comment below.
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-
-
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Email Extractor 5.7.0.4 Keygen42 76 ((BETTER)).md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Email Extractor 5.7.0.4 Keygen42 76 ((BETTER)).md
deleted file mode 100644
index 57241cb8c425a7d720bf11fb9528c3a416e352d3..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Email Extractor 5.7.0.4 Keygen42 76 ((BETTER)).md
+++ /dev/null
@@ -1,90 +0,0 @@
-
-
Email Extractor 5.7.0.4 keygen42 76: What is it and why do you need it?
-
If you are looking for a way to extract email addresses from various sources, such as local files, websites, search engines, etc., you might have come across a software called Email Extractor 5.7.0.4.
But what is Email Extractor 5.7.0.4 and how can you use it? And what is keygen42 76 and why do you need it to activate Email Extractor 5.7.0.4?
-
In this article, we will answer these questions and more, and help you understand what Email Extractor 5.7.0.4 keygen42 76 is and why you might need it.
-
What is Email Extractor 5.7.0.4?
-
Email Extractor 5.7.0.4 is a free all-in-one email spider software that can help you create your customer contact list using your mailbox data.
-
It is a lightweight and powerful utility designed to extract email addresses from various sources, such as local files, websites, search engines, etc.
-
Features and benefits of Email Extractor 5.7.0.4
-
Some of the features and benefits of Email Extractor 5.7.0.4 are:
-
-
-
It can extract email addresses from any text file, such as TXT, CSV, DOC, DOCX, XLS, XLSX, PDF, HTML, etc.
-
It can extract email addresses from any website or web page, such as Google, Yahoo, Bing, Facebook, Twitter, LinkedIn, etc.
-
It can extract email addresses from any search engine query, such as keywords, phrases, URLs, etc.
-
It can extract email addresses from your own email accounts, such as Gmail, Hotmail, Outlook, Yahoo Mail, etc.
-
It can filter out duplicate and invalid email addresses and save only the valid ones.
-
It can export the extracted email addresses to various formats, such as TXT, CSV, XLSX, etc.
-
It can support multiple languages and encoding formats.
-
It can run in the background without affecting your computer performance.
-
-
How to use Email Extractor 5.7.0.4
-
To use Email Extractor 5.7.0.4, you need to follow these steps:
-
-
Download and install Email Extractor 5.7.0.4 from its official website or from other sources.
-
Launch the program and choose the source from which you want to extract email addresses.
-
Select the options and settings according to your preferences and needs.
-
Click on the Start button and wait for the program to scan and extract email addresses.
-
View the results and export them to your desired format.
-
-
What is keygen42 76?
-
Keygen42 76 is a tool that can generate serial keys or activation codes for various software programs, including Email Extractor 5.7.0.4.
-
What is a keygen and how does it work?
-
A keygen, short for key generator, is a software program that can create unique serial keys or activation codes for various software programs, without the need of purchasing a license or a product key from the original developers.
-
A keygen works by analyzing the algorithm or the pattern of the serial keys or activation codes of a specific software program, and then generating new ones that can match the same algorithm or pattern.
-
A keygen can be useful for users who want to use a software program without paying for it, or who have lost their original serial keys or activation codes and need to reinstall the software program.
-
How to use keygen42 76 to activate Email Extractor 5.7.0.4
-
To use keygen42 76 to activate Email Extractor 5.7.0.4, you need to follow these steps:
-
-
Download and run keygen42 76 from its official website or from other sources.
-
Select Email Extractor 5.7.0.4 from the list of software programs that keygen42 76 can generate serial keys or activation codes for.
-
Click on the Generate button and wait for keygen42 76 to create a unique serial key or activation code for Email Extractor 5.7.0.4.
-
Copy the serial key or activation code and paste it into the registration window of Email Extractor 5.7.0.4.
-
Click on the Register button and enjoy using Email Extractor 5.7.0.4 without any limitations.
-
-
Why do you need Email Extractor 5.7.0.4 keygen42 76?
-
You might need Email Extractor 5.7.0.4 keygen42 76 for various reasons, such as:
-
Advantages of using Email Extractor 5.7.0.4 keygen42 76
-
Some of the advantages of using Email Extractor 5.7.0.4 keygen42 76 are:
-
-
You can use Email Extractor 5.7.0.4 for free, without paying any fees or charges to the original developers.
-
You can use Email Extractor 5.7.0.4 without any restrictions or limitations, such as trial periods, watermarks, ads, etc.
-
You can use Email Extractor 5.7.0.4 on multiple devices, such as your laptop, desktop, tablet, etc., without needing to buy multiple licenses or product keys.
-
You can use Email Extractor 5.7.0.4 offline, without needing an internet connection or an online verification process.
-
-
Risks and precautions of using Email Extractor 5.7.0.4 keygen42 76
-
However, using Email Extractor 5.7.0.4 keygen42 76 also comes with some risks and precautions, such as:
-
-
You might violate the intellectual property rights or the terms and conditions of the original developers of Email Extractor 5.7.0.4, and face legal consequences or penalties.
-
You might expose your device or your data to malware, viruses, spyware, etc., that might be hidden in keygen42 76 or in the sources from which you download it.
-
You might not receive any updates, support, or customer service from the original developers of Email Extractor 5.7.0.4, and miss out on new features, bug fixes, security patches, etc.
-
You might not be able to use some features or functions of Email Extractor 5.7.0.4 that require an online connection or verification, such as cloud storage, backup, synchronization, etc.
-
-
Therefore, you should use Email Extractor 5.7 .0.4 keygen42 76 at your own risk and discretion, and only if you cannot afford or access the original version of Email Extractor 5.7.0.4.
-
Conclusion
-
Email Extractor 5.7.0.4 is a free and powerful software program that can help you extract email addresses from various sources, such as local files, websites, search engines, etc.
-
Keygen42 76 is a tool that can generate serial keys or activation codes for Email Extractor 5.7.0.4, allowing you to use it without any limitations or restrictions.
-
However, using Email Extractor 5.7.0.4 keygen42 76 also has some risks and drawbacks, such as legal issues, malware infections, lack of updates and support, etc.
-
Therefore, you should use Email Extractor 5.7.0.4 keygen42 76 only if you really need it and cannot obtain the original version of Email Extractor 5.7.0.4.
-
FAQs
-
Q: Is Email Extractor 5.7.0.4 safe to use?
-
A: Email Extractor 5.7.0.4 is safe to use if you download it from its official website or from other trusted sources. However, if you download it from unknown or suspicious sources, you might get a corrupted or infected file that could harm your device or your data.
-
Q: Is keygen42 76 safe to use?
-
A: Keygen42 76 is not safe to use, as it is an illegal and unauthorized tool that can generate serial keys or activation codes for Email Extractor 5.7.0.4 without the consent of the original developers. Moreover, keygen42 76 might contain malware, viruses, spyware, etc., that could damage your device or your data.
-
Q: How can I get the original version of Email Extractor 5.7.0.4?
-
A: You can get the original version of Email Extractor 5.7.0.4 by purchasing a license or a product key from the official website of Email Extractor 5.7 .0.4 or from other authorized resellers or distributors. You can also get a free trial version of Email Extractor 5.7.0.4 by downloading it from its official website and using it for a limited period of time.
-
Q: How can I contact the original developers of Email Extractor 5.7.0.4?
-
A: You can contact the original developers of Email Extractor 5.7.0.4 by visiting their official website and filling out the contact form or sending them an email. You can also follow them on their social media accounts or subscribe to their newsletter for updates and news.
-
Q: How can I protect my device and my data from malware, viruses, spyware, etc.?
-
A: You can protect your device and your data from malware, viruses, spyware, etc., by following these tips:
-
-
Use a reliable and updated antivirus or anti-malware software on your device and scan it regularly.
-
Download software programs only from official websites or trusted sources and avoid clicking on unknown or suspicious links or attachments.
-
Backup your data regularly and store it in a safe and secure location, such as an external hard drive or a cloud service.
-
Use strong and unique passwords for your accounts and devices and change them frequently.
-
Avoid using public or unsecured Wi-Fi networks or devices and use a VPN or a firewall to encrypt your online traffic.
-
b2dd77e56b
-
-
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Mukta Kannada Serial Cast.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Mukta Kannada Serial Cast.md
deleted file mode 100644
index 646133a55f8d714935826b2c2703a8b1512a6d70..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Mukta Kannada Serial Cast.md
+++ /dev/null
@@ -1,20 +0,0 @@
-
-
Mukta Kannada Serial Cast: A Star-Studded Drama
-
Mukta is a popular Kannada television serial that aired from 2008 to 2013 on ETV Kannada (now Colors Kannada). The serial was directed by T.N. Seetharam, who also played a prominent role in the show. The serial revolved around the lives of two families, the Mukthas and the Patils, who are connected by a common past and a complex web of relationships.
The serial featured a talented cast of actors, who portrayed various characters with depth and nuance. Some of the main cast members were:
-
-
Aishwarya Rajesh as Muktha Patil, the protagonist of the show. She is a strong-willed and independent woman, who strives to balance her personal and professional life.
-
Malavika Avinash as Muktha Muktha, the elder sister of Muktha Patil. She is a successful lawyer and a social activist, who fights for justice and equality.
-
Chirashree Anchan as Muktha Rani, the younger sister of Muktha Patil. She is a sweet and innocent girl, who falls in love with a man from a different caste.
-
Nagaraj Murthy as Muktha Ramaiah, the father of Muktha Patil and Muktha Muktha. He is a retired judge and a respected figure in the society.
-
G.K. Govinda Rao as Patil Ramaiah, the father of Muktha Rani and the antagonist of the show. He is a corrupt politician and a ruthless businessman, who harbors a grudge against the Mukthas.
-
-
Mukta Kannada Serial Cast also included many other supporting actors, who added to the drama and intrigue of the show. The serial was praised for its realistic portrayal of social issues, such as caste discrimination, gender inequality, domestic violence, and corruption. The serial also won several awards, such as the Karnataka State Television Award for Best Serial in 2009 and 2010.
-
-
The serial had a gripping storyline that kept the viewers hooked to every episode. The serial began with the flashback of how Muktha Ramaiah and Patil Ramaiah were once friends and colleagues in the judiciary. However, their friendship turned sour when Patil Ramaiah accused Muktha Ramaiah of taking a bribe and got him suspended from his post. Muktha Ramaiah was later proved innocent, but Patil Ramaiah never apologized for his mistake.
-
-
The serial then shifted to the present day, where Muktha Patil and Muktha Muktha are working as lawyers in their father's law firm. They often clash with Patil Ramaiah and his son, who are involved in various illegal activities. Muktha Rani, who is unaware of the past enmity between the two families, falls in love with Patil Ramaiah's son and elopes with him. This creates more tension and drama between the Mukthas and the Patils.
-
The serial also explored the personal lives of the characters, their struggles, their joys, and their sorrows. The serial showed how Muktha Patil and Muktha Muktha deal with their respective marital issues, how Muktha Rani faces the challenges of living in a hostile environment, how Muktha Ramaiah tries to protect his family from Patil Ramaiah's schemes, and how Patil Ramaiah faces the consequences of his actions.
81aa517590
-
-
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Omnipage Pro 16 Portable Free PORTABLE Download.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Omnipage Pro 16 Portable Free PORTABLE Download.md
deleted file mode 100644
index c1615d3f5838b3ea36572668ec0fa28b51f59abd..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Omnipage Pro 16 Portable Free PORTABLE Download.md
+++ /dev/null
@@ -1,17 +0,0 @@
-
-
How to Convert Scanned Documents into Editable Texts with Omnipage Pro 16 Portable
-
If you have a lot of scanned documents or image PDFs that you want to convert into editable texts, you might be interested in Omnipage Pro 16 Portable. This is a powerful optical character recognition (OCR) software that can recognize text from various sources and formats, such as paper, PDF files, and digital camera pictures. You can then edit the converted texts in your favorite PC applications, such as Word, Excel, PowerPoint, HTML, PDF, etc.
Omnipage Pro 16 Portable is a portable version of Omnipage Pro 16, which means you can run it from a USB flash drive or any other removable media without installing it on your computer. This makes it convenient to use on different computers and devices. You can also save space on your hard drive and avoid potential compatibility issues with other programs.
-
To use Omnipage Pro 16 Portable, you just need to download it from one of the links below and extract the files to your portable device. Then, you can launch the program by double-clicking on the OmniPage.exe file. You will see a user-friendly interface that guides you through the steps of converting your documents.
-
First, you need to select the source of your document. You can choose to scan a paper document using a scanner, open an existing file from your computer or device, or capture an image using a digital camera or smartphone. You can also drag and drop files into the program window.
-
Next, you need to select the output format and destination of your converted document. You can choose from a variety of formats, such as Word, Excel, PowerPoint, HTML, PDF, etc. You can also specify the folder where you want to save the converted file or send it directly to an email address or a cloud service.
-
Finally, you need to start the conversion process by clicking on the Convert button. The program will analyze the text in your document and convert it into editable text. You can then view the converted document in the program window or open it in your preferred application. You can also edit the text within the program using tools such as spell check, find and replace, formatting options, etc.
-
Omnipage Pro 16 Portable is a fast and accurate OCR software that can help you convert scanned documents into editable texts with ease. It supports multiple languages and formats and has a portable feature that makes it convenient to use on any computer or device. You can download Omnipage Pro 16 Portable for free from one of the links below and start converting your documents today.
-
-
One of the main benefits of Omnipage Pro 16 Portable is that it can improve the accuracy and speed of your OCR tasks. According to Nuance, the developer of the software, Omnipage Pro 16 Portable is between 16 and 27 per cent more accurate than the previous version, while at the same time being up to 46 per cent faster. This means you can get more reliable and efficient results from your scanned documents.
-
Another benefit of Omnipage Pro 16 Portable is that it can handle various types of documents and sources. You can scan paper documents using a scanner, open existing files from your computer or device, or capture images using a digital camera or smartphone. You can also drag and drop files into the program window. Omnipage Pro 16 Portable can recognize text from different formats, such as PDF, JPG, TIFF, BMP, etc. It can also compensate for lens distortions and image quality issues that might affect the OCR process.
-
A third benefit of Omnipage Pro 16 Portable is that it can export your converted documents to various formats and destinations. You can choose from a wide range of output formats, such as Word, Excel, PowerPoint, HTML, PDF, etc. You can also specify the folder where you want to save the converted file or send it directly to an email address or a cloud service. Omnipage Pro 16 Portable can also produce documents in Office 2007's XPS format, which is a new XML-based format that preserves the layout and appearance of your documents.
- 7196e7f11a
-
-
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Spotify Mac App Never Works.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Spotify Mac App Never Works.md
deleted file mode 100644
index 2644a3bc62085c30db21e980bd43c021b97b3ed5..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Spotify Mac App Never Works.md
+++ /dev/null
@@ -1,34 +0,0 @@
-
-
How to Fix Spotify Mac App Never Works Issue
-
If you are a Spotify user who owns a Mac computer, you might have encountered the frustrating problem of Spotify Mac app never works. This issue can prevent you from enjoying your favorite music and podcasts on your Mac, and it can also affect your productivity and mood. Fortunately, there are some simple solutions that can help you fix this issue and get Spotify Mac app working again.
In this article, we will explain why Spotify Mac app never works issue happens, and how you can troubleshoot it using different methods. We will also provide some tips on how to prevent this issue from happening again in the future. By following these steps, you should be able to enjoy Spotify on your Mac without any interruptions.
-
Why Spotify Mac App Never Works Issue Happens
-
There are several possible reasons why Spotify Mac app never works issue happens. Some of the most common ones are:
-
-
Spotify app is outdated. If you are using an old version of Spotify app on your Mac, it might not be compatible with the latest updates and features of Spotify. This can cause the app to crash, freeze, or not load properly.
-
Spotify cache is corrupted. Spotify app stores some data in a cache folder on your Mac, which helps it run faster and smoother. However, sometimes this cache folder can get corrupted or overloaded with too much data, which can interfere with the app's performance and functionality.
-
Mac system is incompatible. If you are using an older or unsupported version of Mac OS on your computer, it might not be able to run Spotify app smoothly and efficiently. This can result in compatibility issues and errors that can affect the app's operation.
-
Mac system is overloaded. If you have too many apps or processes running on your Mac at the same time, it might consume too much memory and CPU resources, which can slow down your computer and affect the performance of Spotify app. This can cause the app to lag, stutter, or not respond.
-
Internet connection is unstable. If you have a poor or unreliable internet connection on your Mac, it might not be able to stream or download music and podcasts from Spotify servers. This can cause the app to buffer, skip, or not play any sound.
-
-
How to Fix Spotify Mac App Never Works Issue
-
If you are facing Spotify Mac app never works issue on your computer, don't worry. There are some easy and effective solutions that can help you resolve this issue and get Spotify Mac app working again. Here are some of the methods that you can try:
-
Method 1: Update Spotify App
-
The first thing that you should do when you encounter Spotify Mac app never works issue is to check if you have the latest version of Spotify app installed on your Mac. Updating the app can fix any bugs or glitches that might be causing the issue, and also improve the app's performance and stability. To update Spotify app on your Mac, follow these steps:
-
-
Open Spotify app on your Mac.
-
Click on Spotify in the menu bar at the top of the screen.
-
Select Check for Updates from the drop-down menu.
-
If there is an update available, click on Update Now to download and install it.
-
Restart Spotify app and see if the issue is fixed.
-
-
Method 2: Clear Spotify Cache
-
The next thing that you should do when you face Spotify Mac app never works issue is to clear the cache folder of Spotify app on your Mac. Clearing the cache folder can remove any corrupted or unnecessary data that might be interfering with the app's functionality, and also free up some space on your Mac. To clear Spotify cache on your Mac, follow these steps:
-
-
-
Quit Spotify app if it is running on your Mac.
-
Open Finder on your Mac.
-
Select Go from the menu bar at cec2833e83
-
-
\ No newline at end of file
diff --git a/spaces/ntt123/WaveGRU-Text-To-Speech/sparse_matmul/vector/aligned_malloc.cc b/spaces/ntt123/WaveGRU-Text-To-Speech/sparse_matmul/vector/aligned_malloc.cc
deleted file mode 100644
index 410d268ed838541e13c045b90649884ee4c81cef..0000000000000000000000000000000000000000
--- a/spaces/ntt123/WaveGRU-Text-To-Speech/sparse_matmul/vector/aligned_malloc.cc
+++ /dev/null
@@ -1,46 +0,0 @@
-/*
- * Copyright 2021 Google LLC
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include
-#include
-namespace csrblocksparse {
-
-void Free(void* ptr) { free(ptr); }
-
-void* Malloc(size_t size) { return malloc(size); }
-
-void aligned_free(void* aligned_memory) { Free(aligned_memory); }
-
-void* aligned_malloc(size_t size, int minimum_alignment) {
-#if defined(__ANDROID__)
- return memalign(minimum_alignment, size);
-#else // !defined(__ANDROID__)
- void* ptr = nullptr;
- // posix_memalign requires that the requested alignment be at least
- // sizeof(void*). In this case, fall back on malloc which should return
- // memory aligned to at least the size of a pointer.
- const int required_alignment = sizeof(void*);
- if (minimum_alignment < required_alignment) return Malloc(size);
- int err = posix_memalign(&ptr, minimum_alignment, size);
- if (err != 0) {
- return nullptr;
- } else {
- return ptr;
- }
-#endif
-}
-
-} // namespace csrblocksparse
diff --git a/spaces/ochyai/ochyai_food/app.py b/spaces/ochyai/ochyai_food/app.py
deleted file mode 100644
index d1ea8993da30935fd7ce7d015116b420c9ea3545..0000000000000000000000000000000000000000
--- a/spaces/ochyai/ochyai_food/app.py
+++ /dev/null
@@ -1,141 +0,0 @@
-import gradio as gr
-import openai
-import requests
-import os
-import fileinput
-from dotenv import load_dotenv
-import io
-from PIL import Image
-from stability_sdk import client
-import stability_sdk.interfaces.gooseai.generation.generation_pb2 as generation
-
-title="ochyAI recipe generator"
-inputs_label="どんな料理か教えてくれれば，新しいレシピを考えます"
-outputs_label="ochyAIが返信をします"
-visual_outputs_label="料理のイメージ"
-description="""
-- ※入出力の文字数は最大1000文字程度までを目安に入力してください。解答に120秒くらいかかります．エラーが出た場合はログを開いてエラーメッセージを送ってくれるとochyAIが喜びます
-"""
-
-article = """
-"""
-
-load_dotenv()
-openai.api_key = os.getenv('OPENAI_API_KEY')
-os.environ['STABILITY_HOST'] = 'grpc.stability.ai:443'
-stability_api = client.StabilityInference(
- key=os.getenv('STABILITY_KEY'),
- verbose=True,
-)
-MODEL = "gpt-4"
-
-def get_filetext(filename, cache={}):
- if filename in cache:
- # キャッシュに保存されている場合は、キャッシュからファイル内容を取得する
- return cache[filename]
- else:
- if not os.path.exists(filename):
- raise ValueError(f"ファイル '{filename}' が見つかりませんでした")
- with open(filename, "r") as f:
- text = f.read()
- # ファイル内容をキャッシュする
- cache[filename] = text
- return text
-
-class OpenAI:
-
- @classmethod
- def chat_completion(cls, prompt, start_with=""):
- constraints = get_filetext(filename = "constraints.md")
- template = get_filetext(filename = "template.md")
-
- # ChatCompletion APIに渡すデータを定義する
- data = {
- "model": "gpt-4",
- "messages": [
- {"role": "system", "content": constraints}
- ,{"role": "system", "content": template}
- ,{"role": "assistant", "content": "Sure!"}
- ,{"role": "user", "content": prompt}
- ,{"role": "assistant", "content": start_with}
- ],
- }
-
- # ChatCompletion APIを呼び出す
- response = requests.post(
- "https://api.openai.com/v1/chat/completions",
- headers={
- "Content-Type": "application/json",
- "Authorization": f"Bearer {openai.api_key}"
- },
- json=data
- )
-
- # ChatCompletion APIから返された結果を取得する
- result = response.json()
- print(result)
-
- content = result["choices"][0]["message"]["content"].strip()
-
- split_content = content.split("### Prompt for Visual Expression\n\n")
- if len(split_content) > 1:
- visualize_prompt = split_content[1]
- else:
- visualize_prompt = "vacant dish" # or any default value
-
- #print("split_content:"+split_content)
-
- #if len(split_content) > 1:
- # visualize_prompt = split_content[1]
- #else:
- # visualize_prompt = "vacant dish"
-
- print("visualize_prompt:"+visualize_prompt)
-
- answers = stability_api.generate(
- prompt=visualize_prompt,
- )
-
- for resp in answers:
- for artifact in resp.artifacts:
- if artifact.finish_reason == generation.FILTER:
- print("NSFW")
- if artifact.type == generation.ARTIFACT_IMAGE:
- img = Image.open(io.BytesIO(artifact.binary))
- return [content, img]
-
-class NajiminoAI:
-
- @classmethod
- def generate_emo_prompt(cls, user_message):
- template = get_filetext(filename="template.md")
- prompt = f"""
- {user_message}
- ---
- 上記を元に、下記テンプレートを埋めてください。
- ---
- {template}
- """
- return prompt
-
- @classmethod
- def generate_emo(cls, user_message):
- prompt = NajiminoAI.generate_emo_prompt(user_message);
- start_with = ""
- result = OpenAI.chat_completion(prompt=prompt, start_with=start_with)
- return result
-
-def main():
- iface = gr.Interface(fn=NajiminoAI.generate_emo,
- inputs=gr.Textbox(label=inputs_label),
- outputs=[gr.Textbox(label=inputs_label), gr.Image(label=visual_outputs_label)],
- title=title,
- description=description,
- article=article,
- allow_flagging='never'
- )
-
- iface.launch()
-
-if __name__ == '__main__':
- main()
\ No newline at end of file
diff --git a/spaces/oliver2023/chatgpt-on-wechat/lib/itchat/components/login.py b/spaces/oliver2023/chatgpt-on-wechat/lib/itchat/components/login.py
deleted file mode 100644
index 60ca395f7b78e57e95296affe07c297c6da1d0f3..0000000000000000000000000000000000000000
--- a/spaces/oliver2023/chatgpt-on-wechat/lib/itchat/components/login.py
+++ /dev/null
@@ -1,411 +0,0 @@
-import os
-import time
-import re
-import io
-import threading
-import json
-import xml.dom.minidom
-import random
-import traceback
-import logging
-try:
- from httplib import BadStatusLine
-except ImportError:
- from http.client import BadStatusLine
-
-import requests
-from pyqrcode import QRCode
-
-from .. import config, utils
-from ..returnvalues import ReturnValue
-from ..storage.templates import wrap_user_dict
-from .contact import update_local_chatrooms, update_local_friends
-from .messages import produce_msg
-
-logger = logging.getLogger('itchat')
-
-
-def load_login(core):
- core.login = login
- core.get_QRuuid = get_QRuuid
- core.get_QR = get_QR
- core.check_login = check_login
- core.web_init = web_init
- core.show_mobile_login = show_mobile_login
- core.start_receiving = start_receiving
- core.get_msg = get_msg
- core.logout = logout
-
-
-def login(self, enableCmdQR=False, picDir=None, qrCallback=None,
- loginCallback=None, exitCallback=None):
- if self.alive or self.isLogging:
- logger.warning('itchat has already logged in.')
- return
- self.isLogging = True
- while self.isLogging:
- uuid = push_login(self)
- if uuid:
- qrStorage = io.BytesIO()
- else:
- logger.info('Getting uuid of QR code.')
- while not self.get_QRuuid():
- time.sleep(1)
- logger.info('Downloading QR code.')
- qrStorage = self.get_QR(enableCmdQR=enableCmdQR,
- picDir=picDir, qrCallback=qrCallback)
- # logger.info('Please scan the QR code to log in.')
- isLoggedIn = False
- while not isLoggedIn:
- status = self.check_login()
- if hasattr(qrCallback, '__call__'):
- qrCallback(uuid=self.uuid, status=status,
- qrcode=qrStorage.getvalue())
- if status == '200':
- isLoggedIn = True
- elif status == '201':
- if isLoggedIn is not None:
- logger.info('Please press confirm on your phone.')
- isLoggedIn = None
- time.sleep(7)
- time.sleep(0.5)
- elif status != '408':
- break
- if isLoggedIn:
- break
- elif self.isLogging:
- logger.info('Log in time out, reloading QR code.')
- else:
- return # log in process is stopped by user
- logger.info('Loading the contact, this may take a little while.')
- self.web_init()
- self.show_mobile_login()
- self.get_contact(True)
- if hasattr(loginCallback, '__call__'):
- r = loginCallback()
- else:
- utils.clear_screen()
- if os.path.exists(picDir or config.DEFAULT_QR):
- os.remove(picDir or config.DEFAULT_QR)
- logger.info('Login successfully as %s' % self.storageClass.nickName)
- self.start_receiving(exitCallback)
- self.isLogging = False
-
-
-def push_login(core):
- cookiesDict = core.s.cookies.get_dict()
- if 'wxuin' in cookiesDict:
- url = '%s/cgi-bin/mmwebwx-bin/webwxpushloginurl?uin=%s' % (
- config.BASE_URL, cookiesDict['wxuin'])
- headers = {'User-Agent': config.USER_AGENT}
- r = core.s.get(url, headers=headers).json()
- if 'uuid' in r and r.get('ret') in (0, '0'):
- core.uuid = r['uuid']
- return r['uuid']
- return False
-
-
-def get_QRuuid(self):
- url = '%s/jslogin' % config.BASE_URL
- params = {
- 'appid': 'wx782c26e4c19acffb',
- 'fun': 'new',
- 'redirect_uri': 'https://wx.qq.com/cgi-bin/mmwebwx-bin/webwxnewloginpage?mod=desktop',
- 'lang': 'zh_CN'}
- headers = {'User-Agent': config.USER_AGENT}
- r = self.s.get(url, params=params, headers=headers)
- regx = r'window.QRLogin.code = (\d+); window.QRLogin.uuid = "(\S+?)";'
- data = re.search(regx, r.text)
- if data and data.group(1) == '200':
- self.uuid = data.group(2)
- return self.uuid
-
-
-def get_QR(self, uuid=None, enableCmdQR=False, picDir=None, qrCallback=None):
- uuid = uuid or self.uuid
- picDir = picDir or config.DEFAULT_QR
- qrStorage = io.BytesIO()
- qrCode = QRCode('https://login.weixin.qq.com/l/' + uuid)
- qrCode.png(qrStorage, scale=10)
- if hasattr(qrCallback, '__call__'):
- qrCallback(uuid=uuid, status='0', qrcode=qrStorage.getvalue())
- else:
- with open(picDir, 'wb') as f:
- f.write(qrStorage.getvalue())
- if enableCmdQR:
- utils.print_cmd_qr(qrCode.text(1), enableCmdQR=enableCmdQR)
- else:
- utils.print_qr(picDir)
- return qrStorage
-
-
-def check_login(self, uuid=None):
- uuid = uuid or self.uuid
- url = '%s/cgi-bin/mmwebwx-bin/login' % config.BASE_URL
- localTime = int(time.time())
- params = 'loginicon=true&uuid=%s&tip=1&r=%s&_=%s' % (
- uuid, int(-localTime / 1579), localTime)
- headers = {'User-Agent': config.USER_AGENT}
- r = self.s.get(url, params=params, headers=headers)
- regx = r'window.code=(\d+)'
- data = re.search(regx, r.text)
- if data and data.group(1) == '200':
- if process_login_info(self, r.text):
- return '200'
- else:
- return '400'
- elif data:
- return data.group(1)
- else:
- return '400'
-
-
-def process_login_info(core, loginContent):
- ''' when finish login (scanning qrcode)
- * syncUrl and fileUploadingUrl will be fetched
- * deviceid and msgid will be generated
- * skey, wxsid, wxuin, pass_ticket will be fetched
- '''
- regx = r'window.redirect_uri="(\S+)";'
- core.loginInfo['url'] = re.search(regx, loginContent).group(1)
- headers = {'User-Agent': config.USER_AGENT,
- 'client-version': config.UOS_PATCH_CLIENT_VERSION,
- 'extspam': config.UOS_PATCH_EXTSPAM,
- 'referer': 'https://wx.qq.com/?&lang=zh_CN&target=t'
- }
- r = core.s.get(core.loginInfo['url'],
- headers=headers, allow_redirects=False)
- core.loginInfo['url'] = core.loginInfo['url'][:core.loginInfo['url'].rfind(
- '/')]
- for indexUrl, detailedUrl in (
- ("wx2.qq.com", ("file.wx2.qq.com", "webpush.wx2.qq.com")),
- ("wx8.qq.com", ("file.wx8.qq.com", "webpush.wx8.qq.com")),
- ("qq.com", ("file.wx.qq.com", "webpush.wx.qq.com")),
- ("web2.wechat.com", ("file.web2.wechat.com", "webpush.web2.wechat.com")),
- ("wechat.com", ("file.web.wechat.com", "webpush.web.wechat.com"))):
- fileUrl, syncUrl = ['https://%s/cgi-bin/mmwebwx-bin' %
- url for url in detailedUrl]
- if indexUrl in core.loginInfo['url']:
- core.loginInfo['fileUrl'], core.loginInfo['syncUrl'] = \
- fileUrl, syncUrl
- break
- else:
- core.loginInfo['fileUrl'] = core.loginInfo['syncUrl'] = core.loginInfo['url']
- core.loginInfo['deviceid'] = 'e' + repr(random.random())[2:17]
- core.loginInfo['logintime'] = int(time.time() * 1e3)
- core.loginInfo['BaseRequest'] = {}
- cookies = core.s.cookies.get_dict()
- skey = re.findall('(.*?)', r.text, re.S)[0]
- pass_ticket = re.findall(
- '(.*?)', r.text, re.S)[0]
- core.loginInfo['skey'] = core.loginInfo['BaseRequest']['Skey'] = skey
- core.loginInfo['wxsid'] = core.loginInfo['BaseRequest']['Sid'] = cookies["wxsid"]
- core.loginInfo['wxuin'] = core.loginInfo['BaseRequest']['Uin'] = cookies["wxuin"]
- core.loginInfo['pass_ticket'] = pass_ticket
- # A question : why pass_ticket == DeviceID ?
- # deviceID is only a randomly generated number
-
- # UOS PATCH By luvletter2333, Sun Feb 28 10:00 PM
- # for node in xml.dom.minidom.parseString(r.text).documentElement.childNodes:
- # if node.nodeName == 'skey':
- # core.loginInfo['skey'] = core.loginInfo['BaseRequest']['Skey'] = node.childNodes[0].data
- # elif node.nodeName == 'wxsid':
- # core.loginInfo['wxsid'] = core.loginInfo['BaseRequest']['Sid'] = node.childNodes[0].data
- # elif node.nodeName == 'wxuin':
- # core.loginInfo['wxuin'] = core.loginInfo['BaseRequest']['Uin'] = node.childNodes[0].data
- # elif node.nodeName == 'pass_ticket':
- # core.loginInfo['pass_ticket'] = core.loginInfo['BaseRequest']['DeviceID'] = node.childNodes[0].data
- if not all([key in core.loginInfo for key in ('skey', 'wxsid', 'wxuin', 'pass_ticket')]):
- logger.error(
- 'Your wechat account may be LIMITED to log in WEB wechat, error info:\n%s' % r.text)
- core.isLogging = False
- return False
- return True
-
-
-def web_init(self):
- url = '%s/webwxinit' % self.loginInfo['url']
- params = {
- 'r': int(-time.time() / 1579),
- 'pass_ticket': self.loginInfo['pass_ticket'], }
- data = {'BaseRequest': self.loginInfo['BaseRequest'], }
- headers = {
- 'ContentType': 'application/json; charset=UTF-8',
- 'User-Agent': config.USER_AGENT, }
- r = self.s.post(url, params=params, data=json.dumps(data), headers=headers)
- dic = json.loads(r.content.decode('utf-8', 'replace'))
- # deal with login info
- utils.emoji_formatter(dic['User'], 'NickName')
- self.loginInfo['InviteStartCount'] = int(dic['InviteStartCount'])
- self.loginInfo['User'] = wrap_user_dict(
- utils.struct_friend_info(dic['User']))
- self.memberList.append(self.loginInfo['User'])
- self.loginInfo['SyncKey'] = dic['SyncKey']
- self.loginInfo['synckey'] = '|'.join(['%s_%s' % (item['Key'], item['Val'])
- for item in dic['SyncKey']['List']])
- self.storageClass.userName = dic['User']['UserName']
- self.storageClass.nickName = dic['User']['NickName']
- # deal with contact list returned when init
- contactList = dic.get('ContactList', [])
- chatroomList, otherList = [], []
- for m in contactList:
- if m['Sex'] != 0:
- otherList.append(m)
- elif '@@' in m['UserName']:
- m['MemberList'] = [] # don't let dirty info pollute the list
- chatroomList.append(m)
- elif '@' in m['UserName']:
- # mp will be dealt in update_local_friends as well
- otherList.append(m)
- if chatroomList:
- update_local_chatrooms(self, chatroomList)
- if otherList:
- update_local_friends(self, otherList)
- return dic
-
-
-def show_mobile_login(self):
- url = '%s/webwxstatusnotify?lang=zh_CN&pass_ticket=%s' % (
- self.loginInfo['url'], self.loginInfo['pass_ticket'])
- data = {
- 'BaseRequest': self.loginInfo['BaseRequest'],
- 'Code': 3,
- 'FromUserName': self.storageClass.userName,
- 'ToUserName': self.storageClass.userName,
- 'ClientMsgId': int(time.time()), }
- headers = {
- 'ContentType': 'application/json; charset=UTF-8',
- 'User-Agent': config.USER_AGENT, }
- r = self.s.post(url, data=json.dumps(data), headers=headers)
- return ReturnValue(rawResponse=r)
-
-
-def start_receiving(self, exitCallback=None, getReceivingFnOnly=False):
- self.alive = True
-
- def maintain_loop():
- retryCount = 0
- while self.alive:
- try:
- i = sync_check(self)
- if i is None:
- self.alive = False
- elif i == '0':
- pass
- else:
- msgList, contactList = self.get_msg()
- if msgList:
- msgList = produce_msg(self, msgList)
- for msg in msgList:
- self.msgList.put(msg)
- if contactList:
- chatroomList, otherList = [], []
- for contact in contactList:
- if '@@' in contact['UserName']:
- chatroomList.append(contact)
- else:
- otherList.append(contact)
- chatroomMsg = update_local_chatrooms(
- self, chatroomList)
- chatroomMsg['User'] = self.loginInfo['User']
- self.msgList.put(chatroomMsg)
- update_local_friends(self, otherList)
- retryCount = 0
- except requests.exceptions.ReadTimeout:
- pass
- except:
- retryCount += 1
- logger.error(traceback.format_exc())
- if self.receivingRetryCount < retryCount:
- self.alive = False
- else:
- time.sleep(1)
- self.logout()
- if hasattr(exitCallback, '__call__'):
- exitCallback()
- else:
- logger.info('LOG OUT!')
- if getReceivingFnOnly:
- return maintain_loop
- else:
- maintainThread = threading.Thread(target=maintain_loop)
- maintainThread.setDaemon(True)
- maintainThread.start()
-
-
-def sync_check(self):
- url = '%s/synccheck' % self.loginInfo.get('syncUrl', self.loginInfo['url'])
- params = {
- 'r': int(time.time() * 1000),
- 'skey': self.loginInfo['skey'],
- 'sid': self.loginInfo['wxsid'],
- 'uin': self.loginInfo['wxuin'],
- 'deviceid': self.loginInfo['deviceid'],
- 'synckey': self.loginInfo['synckey'],
- '_': self.loginInfo['logintime'], }
- headers = {'User-Agent': config.USER_AGENT}
- self.loginInfo['logintime'] += 1
- try:
- r = self.s.get(url, params=params, headers=headers,
- timeout=config.TIMEOUT)
- except requests.exceptions.ConnectionError as e:
- try:
- if not isinstance(e.args[0].args[1], BadStatusLine):
- raise
- # will return a package with status '0 -'
- # and value like:
- # 6f:00:8a:9c:09:74:e4:d8:e0:14:bf:96:3a:56:a0:64:1b:a4:25:5d:12:f4:31:a5:30:f1:c6:48:5f:c3:75:6a:99:93
- # seems like status of typing, but before I make further achievement code will remain like this
- return '2'
- except:
- raise
- r.raise_for_status()
- regx = r'window.synccheck={retcode:"(\d+)",selector:"(\d+)"}'
- pm = re.search(regx, r.text)
- if pm is None or pm.group(1) != '0':
- logger.debug('Unexpected sync check result: %s' % r.text)
- return None
- return pm.group(2)
-
-
-def get_msg(self):
- self.loginInfo['deviceid'] = 'e' + repr(random.random())[2:17]
- url = '%s/webwxsync?sid=%s&skey=%s&pass_ticket=%s' % (
- self.loginInfo['url'], self.loginInfo['wxsid'],
- self.loginInfo['skey'], self.loginInfo['pass_ticket'])
- data = {
- 'BaseRequest': self.loginInfo['BaseRequest'],
- 'SyncKey': self.loginInfo['SyncKey'],
- 'rr': ~int(time.time()), }
- headers = {
- 'ContentType': 'application/json; charset=UTF-8',
- 'User-Agent': config.USER_AGENT}
- r = self.s.post(url, data=json.dumps(data),
- headers=headers, timeout=config.TIMEOUT)
- dic = json.loads(r.content.decode('utf-8', 'replace'))
- if dic['BaseResponse']['Ret'] != 0:
- return None, None
- self.loginInfo['SyncKey'] = dic['SyncKey']
- self.loginInfo['synckey'] = '|'.join(['%s_%s' % (item['Key'], item['Val'])
- for item in dic['SyncCheckKey']['List']])
- return dic['AddMsgList'], dic['ModContactList']
-
-
-def logout(self):
- if self.alive:
- url = '%s/webwxlogout' % self.loginInfo['url']
- params = {
- 'redirect': 1,
- 'type': 1,
- 'skey': self.loginInfo['skey'], }
- headers = {'User-Agent': config.USER_AGENT}
- self.s.get(url, params=params, headers=headers)
- self.alive = False
- self.isLogging = False
- self.s.cookies.clear()
- del self.chatroomList[:]
- del self.memberList[:]
- del self.mpList[:]
- return ReturnValue({'BaseResponse': {
- 'ErrMsg': 'logout successfully.',
- 'Ret': 0, }})
diff --git a/spaces/ondrejbiza/isa/invariant_slot_attention/modules/misc.py b/spaces/ondrejbiza/isa/invariant_slot_attention/modules/misc.py
deleted file mode 100644
index e296f881160c0f4cff86e4d86d3cb729978e206c..0000000000000000000000000000000000000000
--- a/spaces/ondrejbiza/isa/invariant_slot_attention/modules/misc.py
+++ /dev/null
@@ -1,340 +0,0 @@
-# coding=utf-8
-# Copyright 2023 The Google Research Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Miscellaneous modules."""
-
-from typing import Any, Callable, Dict, Iterable, Mapping, Optional, Sequence, Tuple, Union
-
-from flax import linen as nn
-import jax
-import jax.numpy as jnp
-
-from invariant_slot_attention.lib import utils
-
-Shape = Tuple[int]
-
-DType = Any
-Array = Any # jnp.ndarray
-ArrayTree = Union[Array, Iterable["ArrayTree"], Mapping[str, "ArrayTree"]] # pytype: disable=not-supported-yet
-ProcessorState = ArrayTree
-PRNGKey = Array
-NestedDict = Dict[str, Any]
-
-
-class Identity(nn.Module):
- """Module that applies the identity function, ignoring any additional args."""
-
- @nn.compact
- def __call__(self, inputs, **args):
- return inputs
-
-
-class Readout(nn.Module):
- """Module for reading out multiple targets from an embedding."""
-
- keys: Sequence[str]
- readout_modules: Sequence[Callable[[], nn.Module]]
- stop_gradient: Optional[Sequence[bool]] = None
-
- @nn.compact
- def __call__(self, inputs, train = False):
- num_targets = len(self.keys)
- assert num_targets >= 1, "Need to have at least one target."
- assert len(self.readout_modules) == num_targets, (
- "len(modules) and len(keys) must match.")
- if self.stop_gradient is not None:
- assert len(self.stop_gradient) == num_targets, (
- "len(stop_gradient) and len(keys) must match.")
- outputs = {}
- for i in range(num_targets):
- if self.stop_gradient is not None and self.stop_gradient[i]:
- x = jax.lax.stop_gradient(inputs)
- else:
- x = inputs
- outputs[self.keys[i]] = self.readout_modules[i]()(x, train) # pytype: disable=not-callable
- return outputs
-
-
-class MLP(nn.Module):
- """Simple MLP with one hidden layer and optional pre-/post-layernorm."""
-
- hidden_size: int
- output_size: Optional[int] = None
- num_hidden_layers: int = 1
- activation_fn: Callable[[Array], Array] = nn.relu
- layernorm: Optional[str] = None
- activate_output: bool = False
- residual: bool = False
-
- @nn.compact
- def __call__(self, inputs, train = False):
- del train # Unused.
-
- output_size = self.output_size or inputs.shape[-1]
-
- x = inputs
-
- if self.layernorm == "pre":
- x = nn.LayerNorm()(x)
-
- for i in range(self.num_hidden_layers):
- x = nn.Dense(self.hidden_size, name=f"dense_mlp_{i}")(x)
- x = self.activation_fn(x)
- x = nn.Dense(output_size, name=f"dense_mlp_{self.num_hidden_layers}")(x)
-
- if self.activate_output:
- x = self.activation_fn(x)
-
- if self.residual:
- x = x + inputs
-
- if self.layernorm == "post":
- x = nn.LayerNorm()(x)
-
- return x
-
-
-class GRU(nn.Module):
- """GRU cell as nn.Module."""
-
- @nn.compact
- def __call__(self, carry, inputs,
- train = False):
- del train # Unused.
- carry, _ = nn.GRUCell()(carry, inputs)
- return carry
-
-
-class Dense(nn.Module):
- """Dense layer as nn.Module accepting "train" flag."""
-
- features: int
- use_bias: bool = True
-
- @nn.compact
- def __call__(self, inputs, train = False):
- del train # Unused.
- return nn.Dense(features=self.features, use_bias=self.use_bias)(inputs)
-
-
-class PositionEmbedding(nn.Module):
- """A module for applying N-dimensional position embedding.
-
- Attr:
- embedding_type: A string defining the type of position embedding to use. One
- of ["linear", "discrete_1d", "fourier", "gaussian_fourier"].
- update_type: A string defining how the input is updated with the position
- embedding. One of ["proj_add", "concat"].
- num_fourier_bases: The number of Fourier bases to use. For embedding_type ==
- "fourier", the embedding dimensionality is 2 x number of position
- dimensions x num_fourier_bases. For embedding_type == "gaussian_fourier",
- the embedding dimensionality is 2 x num_fourier_bases. For embedding_type
- == "linear", this parameter is ignored.
- gaussian_sigma: Standard deviation of sampled Gaussians.
- pos_transform: Optional transform for the embedding.
- output_transform: Optional transform for the combined input and embedding.
- trainable_pos_embedding: Boolean flag for allowing gradients to flow into
- the position embedding, so that the optimizer can update it.
- """
-
- embedding_type: str
- update_type: str
- num_fourier_bases: int = 0
- gaussian_sigma: float = 1.0
- pos_transform: Callable[[], nn.Module] = Identity
- output_transform: Callable[[], nn.Module] = Identity
- trainable_pos_embedding: bool = False
-
- def _make_pos_embedding_tensor(self, rng, input_shape):
- if self.embedding_type == "discrete_1d":
- # An integer tensor in [0, input_shape[-2]-1] reflecting
- # 1D discrete position encoding (encode the second-to-last axis).
- pos_embedding = jnp.broadcast_to(
- jnp.arange(input_shape[-2]), input_shape[1:-1])
- else:
- # A tensor grid in [-1, +1] for each input dimension.
- pos_embedding = utils.create_gradient_grid(input_shape[1:-1], [-1.0, 1.0])
-
- if self.embedding_type == "linear":
- pass
- elif self.embedding_type == "discrete_1d":
- pos_embedding = jax.nn.one_hot(pos_embedding, input_shape[-2])
- elif self.embedding_type == "fourier":
- # NeRF-style Fourier/sinusoidal position encoding.
- pos_embedding = utils.convert_to_fourier_features(
- pos_embedding * jnp.pi, basis_degree=self.num_fourier_bases)
- elif self.embedding_type == "gaussian_fourier":
- # Gaussian Fourier features. Reference: https://arxiv.org/abs/2006.10739
- num_dims = pos_embedding.shape[-1]
- projection = jax.random.normal(
- rng, [num_dims, self.num_fourier_bases]) * self.gaussian_sigma
- pos_embedding = jnp.pi * pos_embedding.dot(projection)
- # A slightly faster implementation of sin and cos.
- pos_embedding = jnp.sin(
- jnp.concatenate([pos_embedding, pos_embedding + 0.5 * jnp.pi],
- axis=-1))
- else:
- raise ValueError("Invalid embedding type provided.")
-
- # Add batch dimension.
- pos_embedding = jnp.expand_dims(pos_embedding, axis=0)
-
- return pos_embedding
-
- @nn.compact
- def __call__(self, inputs):
-
- # Compute the position embedding only in the initial call use the same rng
- # as is used for initializing learnable parameters.
- pos_embedding = self.param("pos_embedding", self._make_pos_embedding_tensor,
- inputs.shape)
-
- if not self.trainable_pos_embedding:
- pos_embedding = jax.lax.stop_gradient(pos_embedding)
-
- # Apply optional transformation on the position embedding.
- pos_embedding = self.pos_transform()(pos_embedding) # pytype: disable=not-callable
-
- # Apply position encoding to inputs.
- if self.update_type == "project_add":
- # Here, we project the position encodings to the same dimensionality as
- # the inputs and add them to the inputs (broadcast along batch dimension).
- # This is roughly equivalent to concatenation of position encodings to the
- # inputs (if followed by a Dense layer), but is slightly more efficient.
- n_features = inputs.shape[-1]
- x = inputs + nn.Dense(n_features, name="dense_pe_0")(pos_embedding)
- elif self.update_type == "concat":
- # Repeat the position embedding along the first (batch) dimension.
- pos_embedding = jnp.broadcast_to(
- pos_embedding, shape=inputs.shape[:-1] + pos_embedding.shape[-1:])
- # concatenate along the channel dimension.
- x = jnp.concatenate((inputs, pos_embedding), axis=-1)
- else:
- raise ValueError("Invalid update type provided.")
-
- # Apply optional output transformation.
- x = self.output_transform()(x) # pytype: disable=not-callable
- return x
-
-
-class RelativePositionEmbedding(nn.Module):
- """A module for applying embedding of input position relative to slots.
-
- Attr
- update_type: A string defining how the input is updated with the position
- embedding. One of ["proj_add", "concat"].
- embedding_type: A string defining the type of position embedding to use.
- Currently only "linear" is supported.
- num_fourier_bases: The number of Fourier bases to use. For embedding_type ==
- "fourier", the embedding dimensionality is 2 x number of position
- dimensions x num_fourier_bases. For embedding_type == "gaussian_fourier",
- the embedding dimensionality is 2 x num_fourier_bases. For embedding_type
- == "linear", this parameter is ignored.
- gaussian_sigma: Standard deviation of sampled Gaussians.
- pos_transform: Optional transform for the embedding.
- output_transform: Optional transform for the combined input and embedding.
- trainable_pos_embedding: Boolean flag for allowing gradients to flow into
- the position embedding, so that the optimizer can update it.
- """
-
- update_type: str
- embedding_type: str = "linear"
- num_fourier_bases: int = 0
- gaussian_sigma: float = 1.0
- pos_transform: Callable[[], nn.Module] = Identity
- output_transform: Callable[[], nn.Module] = Identity
- trainable_pos_embedding: bool = False
- scales_factor: float = 1.0
-
- def _make_pos_embedding_tensor(self, rng, input_shape):
-
- # A tensor grid in [-1, +1] for each input dimension.
- pos_embedding = utils.create_gradient_grid(input_shape[1:-1], [-1.0, 1.0])
-
- # Add batch dimension.
- pos_embedding = jnp.expand_dims(pos_embedding, axis=0)
-
- return pos_embedding
-
- @nn.compact
- def __call__(self, inputs, slot_positions,
- slot_scales = None,
- slot_rotm = None):
-
- # Compute the position embedding only in the initial call use the same rng
- # as is used for initializing learnable parameters.
- pos_embedding = self.param("pos_embedding", self._make_pos_embedding_tensor,
- inputs.shape)
-
- if not self.trainable_pos_embedding:
- pos_embedding = jax.lax.stop_gradient(pos_embedding)
-
- # Relativize pos_embedding with respect to slot positions
- # and optionally slot scales.
- slot_positions = jnp.expand_dims(
- jnp.expand_dims(slot_positions, axis=-2), axis=-2)
- if slot_scales is not None:
- slot_scales = jnp.expand_dims(
- jnp.expand_dims(slot_scales, axis=-2), axis=-2)
-
- if self.embedding_type == "linear":
- pos_embedding = pos_embedding - slot_positions
- if slot_rotm is not None:
- pos_embedding = self.transform(slot_rotm, pos_embedding)
- if slot_scales is not None:
- # Scales are usually small so the grid might get too large.
- pos_embedding = pos_embedding / self.scales_factor
- pos_embedding = pos_embedding / slot_scales
- else:
- raise ValueError("Invalid embedding type provided.")
-
- # Apply optional transformation on the position embedding.
- pos_embedding = self.pos_transform()(pos_embedding) # pytype: disable=not-callable
-
- # Define intermediate for logging.
- pos_embedding = Identity(name="pos_emb")(pos_embedding)
-
- # Apply position encoding to inputs.
- if self.update_type == "project_add":
- # Here, we project the position encodings to the same dimensionality as
- # the inputs and add them to the inputs (broadcast along batch dimension).
- # This is roughly equivalent to concatenation of position encodings to the
- # inputs (if followed by a Dense layer), but is slightly more efficient.
- n_features = inputs.shape[-1]
- x = inputs + nn.Dense(n_features, name="dense_pe_0")(pos_embedding)
- elif self.update_type == "concat":
- # Repeat the position embedding along the first (batch) dimension.
- pos_embedding = jnp.broadcast_to(
- pos_embedding, shape=inputs.shape[:-1] + pos_embedding.shape[-1:])
- # concatenate along the channel dimension.
- x = jnp.concatenate((inputs, pos_embedding), axis=-1)
- else:
- raise ValueError("Invalid update type provided.")
-
- # Apply optional output transformation.
- x = self.output_transform()(x) # pytype: disable=not-callable
- return x
-
- @classmethod
- def transform(cls, rot, coords):
- # The coordinate grid coords is in the (y, x) format, so we need to swap
- # the coordinates on the input and output.
- coords = jnp.stack([coords[Ellipsis, 1], coords[Ellipsis, 0]], axis=-1)
- # Equivalent to inv(R) * coords^T = R^T * coords^T = (coords * R)^T.
- # We are multiplying by the inverse of the rotation matrix because
- # we are rotating the coordinate grid *against* the rotation of the object.
- new_coords = jnp.einsum("...hij,...jk->...hik", coords, rot)
- # Swap coordinates again.
- return jnp.stack([new_coords[Ellipsis, 1], new_coords[Ellipsis, 0]], axis=-1)
diff --git a/spaces/opetrova/face-frontalization/app.py b/spaces/opetrova/face-frontalization/app.py
deleted file mode 100644
index ab0292e40af0edfc26e2af2e4ae5adcffa5a9dc9..0000000000000000000000000000000000000000
--- a/spaces/opetrova/face-frontalization/app.py
+++ /dev/null
@@ -1,47 +0,0 @@
-from huggingface_hub import hf_hub_url, hf_hub_download
-
-import gradio as gr
-import numpy as np
-import requests
-
-import torch
-from torchvision import transforms
-from torch.autograd import Variable
-
-from PIL import Image
-
-import warnings
-warnings.filterwarnings('ignore')
-
-path_to_model = hf_hub_download(repo_id="opetrova/face-frontalization", filename="generator_v0.pt")
-
-# Download network.py into the current directory
-network_url = hf_hub_url(repo_id="opetrova/face-frontalization", filename="network.py")
-r = requests.get(network_url, allow_redirects=True)
-open('network.py', 'wb').write(r.content)
-
-saved_model = torch.load(path_to_model, map_location=torch.device('cpu'))
-
-def frontalize(image):
-
- # Convert the test image to a [1, 3, 128, 128]-shaped torch tensor
- # (as required by the frontalization model)
- preprocess = transforms.Compose((transforms.ToPILImage(),
- transforms.Resize(size = (128, 128)),
- transforms.ToTensor()))
- input_tensor = torch.unsqueeze(preprocess(image), 0)
-
- # Use the saved model to generate an output (whose values go between -1 and 1,
- # and this will need to get fixed before the output is displayed)
- generated_image = saved_model(Variable(input_tensor.type('torch.FloatTensor')))
- generated_image = generated_image.detach().squeeze().permute(1, 2, 0).numpy()
- generated_image = (generated_image + 1.0) / 2.0
-
- return generated_image
-
-iface = gr.Interface(frontalize, gr.inputs.Image(type="numpy"), "image",
- title='Face Frontalization',
- description='PyTorch implementation of a supervised GAN (see blog post)',
- examples=["amos.png", "clarissa.png"],
-)
-iface.launch()
\ No newline at end of file
diff --git a/spaces/owaiskha9654/Multi-Label-Classification-of-Pubmed-Articles/app.py b/spaces/owaiskha9654/Multi-Label-Classification-of-Pubmed-Articles/app.py
deleted file mode 100644
index d5e7ac62f6b548dcbc60085885e9cabcdeaebf80..0000000000000000000000000000000000000000
--- a/spaces/owaiskha9654/Multi-Label-Classification-of-Pubmed-Articles/app.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import torch
-import numpy as np
-import gradio as gr
-from typing import Dict
-from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
-from transformers import BertForSequenceClassification,BertTokenizer
-
-
-num_labels=14
-#Loading Model and Tokenizer from Hugging Face Spaces
-model = BertForSequenceClassification.from_pretrained("owaiskha9654/Multi-Label-Classification-of-PubMed-Articles", num_labels=num_labels)
-tokenizer = BertTokenizer.from_pretrained('owaiskha9654/Multi-Label-Classification-of-PubMed-Articles', do_lower_case=True)
-
-
-def Multi_Label_Classification_of_Pubmed_Articles(model_input: str) -> Dict[str, float]: #This wrapper function will pass the article into the model
- dict_custom={}
- Preprocess_part1=model_input[:len(model_input)] #splitting inputext into 2 parts
- Preprocess_part2=model_input[len(model_input):]
- dict1=tokenizer.encode_plus(Preprocess_part1,max_length=1024,padding=True,truncation=True)
- dict2=tokenizer.encode_plus(Preprocess_part2,max_length=1024,padding=True,truncation=True)
-
- dict_custom['input_ids']=[dict1['input_ids'],dict1['input_ids']]
- dict_custom['token_type_ids']=[dict1['token_type_ids'],dict1['token_type_ids']]
- dict_custom['attention_mask']=[dict1['attention_mask'],dict1['attention_mask']]
-
- outs = model(torch.tensor(dict_custom['input_ids']), token_type_ids=None, attention_mask=torch.tensor(dict_custom['attention_mask']))
- b_logit_pred = outs[0]
- pred_label = torch.sigmoid(b_logit_pred)
-
- ret ={
- "Anatomy [A]": float(pred_label[0][0]),
- "Organisms [B]": float(pred_label[0][1]),
- "Diseases [C]": float(pred_label[0][2]),
- "Chemicals and Drugs [D]": float(pred_label[0][3]),
- "Analytical, Diagnostic and Therapeutic Techniques, and Equipment [E]": float(pred_label[0][4]),
- "Psychiatry and Psychology [F]": float(pred_label[0][5]),
- "Phenomena and Processes [G]": float(pred_label[0][6]),
- "Disciplines and Occupations [H]": float(pred_label[0][7]),
- "Anthropology, Education, Sociology, and Social Phenomena [I]": float(pred_label[0][8]),
- "Technology, Industry, and Agriculture [J]": float(pred_label[0][9]),
- "Information Science [L]": float(pred_label[0][10]),
- "Named Groups [M]": float(pred_label[0][11]),
- "Health Care [N]": float(pred_label[0][12]),
- "Geographicals [Z]": float(pred_label[0][13])}
- return ret
-
-
-model_input = gr.Textbox("Input text here (Note: This model is trained to classify Medical Articles)", show_label=False)
-model_output = gr.Label("Multi Label MeSH(Medical Subheadings) Result", num_top_classes=6, show_label=True, label="MeSH(Medical Subheadings) Labels assigned to this article")
-
-
-examples = [
- (
- "A case of a patient with type 1 neurofibromatosis associated with popliteal and coronary artery aneurysms is described in which cross-sectional",
- "imaging provided diagnostic information.",
- "The aim of this study was to compare the exercise intensity and competition load during Time Trial (TT), Flat (FL), Medium Mountain (MM) and High ",
- "Mountain (HM) stages based heart rate (HR) and session rating of perceived exertion (RPE).METHODS: We monitored both HR and RPE of 12 professional ",
- "cyclists during two consecutive 21-day cycling races in order to analyze the exercise intensity and competition load (TRIMPHR and TRIMPRPE).",
- "RESULTS:The highest (P<0.05) mean HR was found in TT (169±2 bpm) versus those observed in FL (135±1 bpm), MM (139±3 bpm), HM (143±1 bpm)"
- ),
- (
- "The association of body mass index (BMI) with blood pressure may be stronger in Asian than non-Asian populations, however, longitudinal studies ",
- "with direct comparisons between ethnicities are lacking. We compared the relationship of BMI with incident hypertension over approximately 9.5 years",
- " of follow-up in young (24-39 years) and middle-aged (45-64 years) Chinese Asians (n=5354), American Blacks (n=6076) and American Whites (n=13451).",
- "We estimated risk differences using logistic regression models and calculated adjusted incidences and incidence differences. ",
- "To facilitate comparisons across ethnicities, standardized estimates were calculated using mean covariate values for age, sex, smoking, education",
- "and field center, and included the quadratic terms for BMI and age. Weighted least-squares regression models with were constructed to summarize",
- "ethnic-specific incidence differences across BMI. Wald statistics and p-values were calculated based on chi-square distributions. The association of",
- "BMI with the incidence difference for hypertension was steeper in Chinese (p<0.05) than in American populations during young and middle-adulthood.",
- "For example, at a BMI of 25 vs 21 kg/m2 the adjusted incidence differences per 1000 persons (95% CI) in young adults with a BMI of 25 vs those with",
- "a BMI of 21 was 83 (36- 130) for Chinese, 50 (26-74) for Blacks and 30 (12-48) for Whites"
- )
-]
-
-title = "Multi Label Classification of PubMed Articles using BIO-BERT"
-description = "The traditional machine learning models give a lot of pain when we do not have sufficient labeled data for the specific task or domain we care about to train a reliable model. Transfer learning allows us to deal with these scenarios by leveraging the already existing labeled data of some related task or domain. We try to store this knowledge gained in solving the source task in the source domain and apply it to our problem of interest. In this work, I have utilized Transfer Learning utilizing BIO BERT model to fine tune on PubMed MultiLabel classification Dataset from BIOASQ Task A."
-text1 = (
- "
Author: Owais Ahmad Data Scientist at Thoucentric Visit Profile
"
-)
-
-
-
-app = gr.Interface(
- Multi_Label_Classification_of_Pubmed_Articles,
- inputs=model_input,
- outputs=model_output,
- examples=examples,
- title=title,
- description=description,
- article=text1,
- allow_flagging='never',
- analytics_enabled=False,
-)
-
-app.launch()
\ No newline at end of file
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/optimization/opt_overview.md b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/optimization/opt_overview.md
deleted file mode 100644
index 1f809bb011cea1364b26a5a60019b9ca9b46fe0e..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/optimization/opt_overview.md
+++ /dev/null
@@ -1,17 +0,0 @@
-
-
-# Overview
-
-Generating high-quality outputs is computationally intensive, especially during each iterative step where you go from a noisy output to a less noisy output. One of 🤗 Diffuser's goal is to make this technology widely accessible to everyone, which includes enabling fast inference on consumer and specialized hardware.
-
-This section will cover tips and tricks - like half-precision weights and sliced attention - for optimizing inference speed and reducing memory-consumption. You'll also learn how to speed up your PyTorch code with [`torch.compile`](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) or [ONNX Runtime](https://onnxruntime.ai/docs/), and enable memory-efficient attention with [xFormers](https://facebookresearch.github.io/xformers/). There are also guides for running inference on specific hardware like Apple Silicon, and Intel or Habana processors.
\ No newline at end of file
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/using-diffusers/controlnet.md b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/using-diffusers/controlnet.md
deleted file mode 100644
index be02e999e1b86717920cef85a37123dd1cd6b439..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/using-diffusers/controlnet.md
+++ /dev/null
@@ -1,529 +0,0 @@
-# ControlNet
-
-ControlNet is a type of model for controlling image diffusion models by conditioning the model with an additional input image. There are many types of conditioning inputs (canny edge, user sketching, human pose, depth, and more) you can use to control a diffusion model. This is hugely useful because it affords you greater control over image generation, making it easier to generate specific images without experimenting with different text prompts or denoising values as much.
-
-
-
-Check out Section 3.5 of the [ControlNet](https://huggingface.co/papers/2302.05543) paper for a list of ControlNet implementations on various conditioning inputs. You can find the official Stable Diffusion ControlNet conditioned models on [lllyasviel](https://huggingface.co/lllyasviel)'s Hub profile, and more [community-trained](https://huggingface.co/models?other=stable-diffusion&other=controlnet) ones on the Hub.
-
-For Stable Diffusion XL (SDXL) ControlNet models, you can find them on the 🤗 [Diffusers](https://huggingface.co/diffusers) Hub organization, or you can browse [community-trained](https://huggingface.co/models?other=stable-diffusion-xl&other=controlnet) ones on the Hub.
-
-
-
-A ControlNet model has two sets of weights (or blocks) connected by a zero-convolution layer:
-
-- a *locked copy* keeps everything a large pretrained diffusion model has learned
-- a *trainable copy* is trained on the additional conditioning input
-
-Since the locked copy preserves the pretrained model, training and implementing a ControlNet on a new conditioning input is as fast as finetuning any other model because you aren't training the model from scratch.
-
-This guide will show you how to use ControlNet for text-to-image, image-to-image, inpainting, and more! There are many types of ControlNet conditioning inputs to choose from, but in this guide we'll only focus on several of them. Feel free to experiment with other conditioning inputs!
-
-Before you begin, make sure you have the following libraries installed:
-
-```py
-# uncomment to install the necessary libraries in Colab
-#!pip install diffusers transformers accelerate safetensors opencv-python
-```
-
-## Text-to-image
-
-For text-to-image, you normally pass a text prompt to the model. But with ControlNet, you can specify an additional conditioning input. Let's condition the model with a canny image, a white outline of an image on a black background. This way, the ControlNet can use the canny image as a control to guide the model to generate an image with the same outline.
-
-Load an image and use the [opencv-python](https://github.com/opencv/opencv-python) library to extract the canny image:
-
-```py
-from diffusers import StableDiffusionControlNetPipeline
-from diffusers.utils import load_image
-from PIL import Image
-import cv2
-import numpy as np
-
-image = load_image(
- "https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png"
-)
-
-image = np.array(image)
-
-low_threshold = 100
-high_threshold = 200
-
-image = cv2.Canny(image, low_threshold, high_threshold)
-image = image[:, :, None]
-image = np.concatenate([image, image, image], axis=2)
-canny_image = Image.fromarray(image)
-```
-
-
-
-
- original image
-
-
-
- canny image
-
-
-
-Next, load a ControlNet model conditioned on canny edge detection and pass it to the [`StableDiffusionControlNetPipeline`]. Use the faster [`UniPCMultistepScheduler`] and enable model offloading to speed up inference and reduce memory usage.
-
-```py
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
-import torch
-
-controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16, use_safetensors=True)
-pipe = StableDiffusionControlNetPipeline.from_pretrained(
- "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True
-).to("cuda")
-
-pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
-pipe.enable_model_cpu_offload()
-```
-
-Now pass your prompt and canny image to the pipeline:
-
-```py
-output = pipe(
- "the mona lisa", image=canny_image
-).images[0]
-```
-
-
-
-
-
-## Image-to-image
-
-For image-to-image, you'd typically pass an initial image and a prompt to the pipeline to generate a new image. With ControlNet, you can pass an additional conditioning input to guide the model. Let's condition the model with a depth map, an image which contains spatial information. This way, the ControlNet can use the depth map as a control to guide the model to generate an image that preserves spatial information.
-
-You'll use the [`StableDiffusionControlNetImg2ImgPipeline`] for this task, which is different from the [`StableDiffusionControlNetPipeline`] because it allows you to pass an initial image as the starting point for the image generation process.
-
-Load an image and use the `depth-estimation` [`~transformers.Pipeline`] from 🤗 Transformers to extract the depth map of an image:
-
-```py
-import torch
-import numpy as np
-
-from transformers import pipeline
-from diffusers.utils import load_image
-
-image = load_image(
- "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet-img2img.jpg"
-).resize((768, 768))
-
-
-def get_depth_map(image, depth_estimator):
- image = depth_estimator(image)["depth"]
- image = np.array(image)
- image = image[:, :, None]
- image = np.concatenate([image, image, image], axis=2)
- detected_map = torch.from_numpy(image).float() / 255.0
- depth_map = detected_map.permute(2, 0, 1)
- return depth_map
-
-depth_estimator = pipeline("depth-estimation")
-depth_map = get_depth_map(image, depth_estimator).unsqueeze(0).half().to("cuda")
-```
-
-Next, load a ControlNet model conditioned on depth maps and pass it to the [`StableDiffusionControlNetImg2ImgPipeline`]. Use the faster [`UniPCMultistepScheduler`] and enable model offloading to speed up inference and reduce memory usage.
-
-```py
-from diffusers import StableDiffusionControlNetImg2ImgPipeline, ControlNetModel, UniPCMultistepScheduler
-import torch
-
-controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11f1p_sd15_depth", torch_dtype=torch.float16, use_safetensors=True)
-pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
- "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True
-).to("cuda")
-
-pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
-pipe.enable_model_cpu_offload()
-```
-
-Now pass your prompt, initial image, and depth map to the pipeline:
-
-```py
-output = pipe(
- "lego batman and robin", image=image, control_image=depth_map,
-).images[0]
-```
-
-
-
-
- original image
-
-
-
- generated image
-
-
-
-
-## Inpainting
-
-For inpainting, you need an initial image, a mask image, and a prompt describing what to replace the mask with. ControlNet models allow you to add another control image to condition a model with. Let’s condition the model with a canny image, a white outline of an image on a black background. This way, the ControlNet can use the canny image as a control to guide the model to generate an image with the same outline.
-
-Load an initial image and a mask image:
-
-```py
-from diffusers import StableDiffusionControlNetInpaintPipeline, ControlNetModel, UniPCMultistepScheduler
-from diffusers.utils import load_image
-import numpy as np
-import torch
-
-init_image = load_image(
- "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet-inpaint.jpg"
-)
-init_image = init_image.resize((512, 512))
-
-mask_image = load_image(
- "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet-inpaint-mask.jpg"
-)
-mask_image = mask_image.resize((512, 512))
-```
-
-Create a function to prepare the control image from the initial and mask images. This'll create a tensor to mark the pixels in `init_image` as masked if the corresponding pixel in `mask_image` is over a certain threshold.
-
-```py
-def make_inpaint_condition(image, image_mask):
- image = np.array(image.convert("RGB")).astype(np.float32) / 255.0
- image_mask = np.array(image_mask.convert("L")).astype(np.float32) / 255.0
-
- assert image.shape[0:1] == image_mask.shape[0:1]
- image[image_mask > 0.5] = 1.0 # set as masked pixel
- image = np.expand_dims(image, 0).transpose(0, 3, 1, 2)
- image = torch.from_numpy(image)
- return image
-
-control_image = make_inpaint_condition(init_image, mask_image)
-```
-
-
-
-
- original image
-
-
-
- mask image
-
-
-
-Load a ControlNet model conditioned on inpainting and pass it to the [`StableDiffusionControlNetInpaintPipeline`]. Use the faster [`UniPCMultistepScheduler`] and enable model offloading to speed up inference and reduce memory usage.
-
-```py
-from diffusers import StableDiffusionControlNetInpaintPipeline, ControlNetModel, UniPCMultistepScheduler
-import torch
-
-controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpaint", torch_dtype=torch.float16, use_safetensors=True)
-pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
- "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True
-).to("cuda")
-
-pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
-pipe.enable_model_cpu_offload()
-```
-
-Now pass your prompt, initial image, mask image, and control image to the pipeline:
-
-```py
-output = pipe(
- "corgi face with large ears, detailed, pixar, animated, disney",
- num_inference_steps=20,
- eta=1.0,
- image=init_image,
- mask_image=mask_image,
- control_image=control_image,
-).images[0]
-```
-
-
-
-
-
-## Guess mode
-
-[Guess mode](https://github.com/lllyasviel/ControlNet/discussions/188) does not require supplying a prompt to a ControlNet at all! This forces the ControlNet encoder to do it's best to "guess" the contents of the input control map (depth map, pose estimation, canny edge, etc.).
-
-Guess mode adjusts the scale of the output residuals from a ControlNet by a fixed ratio depending on the block depth. The shallowest `DownBlock` corresponds to 0.1, and as the blocks get deeper, the scale increases exponentially such that the scale of the `MidBlock` output becomes 1.0.
-
-
-
-Guess mode does not have any impact on prompt conditioning and you can still provide a prompt if you want.
-
-
-
-Set `guess_mode=True` in the pipeline, and it is [recommended](https://github.com/lllyasviel/ControlNet#guess-mode--non-prompt-mode) to set the `guidance_scale` value between 3.0 and 5.0.
-
-```py
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
-import torch
-
-controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", use_safetensors=True)
-pipe = StableDiffusionControlNetPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", controlnet=controlnet, use_safetensors=True).to(
- "cuda"
-)
-image = pipe("", image=canny_image, guess_mode=True, guidance_scale=3.0).images[0]
-image
-```
-
-
-
-
- regular mode with prompt
-
-
-
- guess mode without prompt
-
-
-
-## ControlNet with Stable Diffusion XL
-
-There aren't too many ControlNet models compatible with Stable Diffusion XL (SDXL) at the moment, but we've trained two full-sized ControlNet models for SDXL conditioned on canny edge detection and depth maps. We're also experimenting with creating smaller versions of these SDXL-compatible ControlNet models so it is easier to run on resource-constrained hardware. You can find these checkpoints on the 🤗 [Diffusers](https://huggingface.co/diffusers) Hub organization!
-
-Let's use a SDXL ControlNet conditioned on canny images to generate an image. Start by loading an image and prepare the canny image:
-
-```py
-from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel, AutoencoderKL
-from diffusers.utils import load_image
-from PIL import Image
-import cv2
-import numpy as np
-
-image = load_image(
- "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png"
-)
-
-image = np.array(image)
-
-low_threshold = 100
-high_threshold = 200
-
-image = cv2.Canny(image, low_threshold, high_threshold)
-image = image[:, :, None]
-image = np.concatenate([image, image, image], axis=2)
-canny_image = Image.fromarray(image)
-canny_image
-```
-
-
-
-
- original image
-
-
-
- canny image
-
-
-
-Load a SDXL ControlNet model conditioned on canny edge detection and pass it to the [`StableDiffusionXLControlNetPipeline`]. You can also enable model offloading to reduce memory usage.
-
-```py
-controlnet = ControlNetModel.from_pretrained(
- "diffusers/controlnet-canny-sdxl-1.0",
- torch_dtype=torch.float16,
- use_safetensors=True
-)
-vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16, use_safetensors=True)
-pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
- "stabilityai/stable-diffusion-xl-base-1.0",
- controlnet=controlnet,
- vae=vae,
- torch_dtype=torch.float16,
- use_safetensors=True
-)
-pipe.enable_model_cpu_offload()
-```
-
-Now pass your prompt (and optionally a negative prompt if you're using one) and canny image to the pipeline:
-
-
-
-The [`controlnet_conditioning_scale`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/controlnet#diffusers.StableDiffusionControlNetPipeline.__call__.controlnet_conditioning_scale) parameter determines how much weight to assign to the conditioning inputs. A value of 0.5 is recommended for good generalization, but feel free to experiment with this number!
-
-
-
-```py
-prompt = "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting"
-negative_prompt = 'low quality, bad quality, sketches'
-
-images = pipe(
- prompt,
- negative_prompt=negative_prompt,
- image=image,
- controlnet_conditioning_scale=0.5,
-).images[0]
-images
-```
-
-
-
-
-
-You can use [`StableDiffusionXLControlNetPipeline`] in guess mode as well by setting the parameter to `True`:
-
-```py
-from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel, AutoencoderKL
-from diffusers.utils import load_image
-import numpy as np
-import torch
-
-import cv2
-from PIL import Image
-
-prompt = "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting"
-negative_prompt = "low quality, bad quality, sketches"
-
-image = load_image(
- "https://hf.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png"
-)
-
-controlnet = ControlNetModel.from_pretrained(
- "diffusers/controlnet-canny-sdxl-1.0", torch_dtype=torch.float16, use_safetensors=True
-)
-vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16, use_safetensors=True)
-pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
- "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, vae=vae, torch_dtype=torch.float16, use_safetensors=True
-)
-pipe.enable_model_cpu_offload()
-
-image = np.array(image)
-image = cv2.Canny(image, 100, 200)
-image = image[:, :, None]
-image = np.concatenate([image, image, image], axis=2)
-canny_image = Image.fromarray(image)
-
-image = pipe(
- prompt, controlnet_conditioning_scale=0.5, image=canny_image, guess_mode=True,
-).images[0]
-```
-
-### MultiControlNet
-
-
-
-Replace the SDXL model with a model like [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) to use multiple conditioning inputs with Stable Diffusion models.
-
-
-
-You can compose multiple ControlNet conditionings from different image inputs to create a *MultiControlNet*. To get better results, it is often helpful to:
-
-1. mask conditionings such that they don't overlap (for example, mask the area of a canny image where the pose conditioning is located)
-2. experiment with the [`controlnet_conditioning_scale`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/controlnet#diffusers.StableDiffusionControlNetPipeline.__call__.controlnet_conditioning_scale) parameter to determine how much weight to assign to each conditioning input
-
-In this example, you'll combine a canny image and a human pose estimation image to generate a new image.
-
-Prepare the canny image conditioning:
-
-```py
-from diffusers.utils import load_image
-from PIL import Image
-import numpy as np
-import cv2
-
-canny_image = load_image(
- "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/landscape.png"
-)
-canny_image = np.array(canny_image)
-
-low_threshold = 100
-high_threshold = 200
-
-canny_image = cv2.Canny(canny_image, low_threshold, high_threshold)
-
-# zero out middle columns of image where pose will be overlayed
-zero_start = canny_image.shape[1] // 4
-zero_end = zero_start + canny_image.shape[1] // 2
-canny_image[:, zero_start:zero_end] = 0
-
-canny_image = canny_image[:, :, None]
-canny_image = np.concatenate([canny_image, canny_image, canny_image], axis=2)
-canny_image = Image.fromarray(canny_image).resize((1024, 1024))
-```
-
-
'
-examples = [
- ["Dom Pedro II foi o segundo e último monarca do Império do Brasil, reinando por mais de 58 anos.", "Quem foi Dom Pedro II?"],
- ["A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de COVID-19, uma doença respiratória aguda causada pelo coronavírus da síndrome respiratória aguda grave 2 (SARS-CoV-2). A doença foi identificada pela primeira vez em Wuhan, na província de Hubei, República Popular da China, em 1 de dezembro de 2019, mas o primeiro caso foi reportado em 31 de dezembro do mesmo ano.", "Quando começou a pandemia de Covid-19 no mundo?"],
- ["A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de COVID-19, uma doença respiratória aguda causada pelo coronavírus da síndrome respiratória aguda grave 2 (SARS-CoV-2). A doença foi identificada pela primeira vez em Wuhan, na província de Hubei, República Popular da China, em 1 de dezembro de 2019, mas o primeiro caso foi reportado em 31 de dezembro do mesmo ano.", "Onde começou a pandemia de Covid-19?"]
- ]
-
-demo = gr.Interface(
- fn=get_answer,
- inputs=[
- gr.Textbox(lines=7, label="Context"),
- gr.Textbox(lines=2, label="Question")
- ],
- outputs=[
- gr.Image(label="Prediction times", type="pil"),
- gr.JSON(label="Results"),
- ],
- title=title,
- description=description,
- examples=examples,
- allow_flagging="never")
-
-if __name__ == "__main__":
- demo.launch()
\ No newline at end of file
diff --git a/spaces/pikto/ELITE-ChatGPT-Streamlit-2/README.md b/spaces/pikto/ELITE-ChatGPT-Streamlit-2/README.md
deleted file mode 100644
index 733fad10c1225a73950657cfbcf1118eaffc8bf3..0000000000000000000000000000000000000000
--- a/spaces/pikto/ELITE-ChatGPT-Streamlit-2/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: ChatGPT Streamlit 2
-emoji: 😻😻😻
-colorFrom: purple
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: awacke1/ChatGPT-Streamlit-2
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/pkiage/time_series_decomposition_demo/test_environment.py b/spaces/pkiage/time_series_decomposition_demo/test_environment.py
deleted file mode 100644
index d0ac4a7252c9cf9c057ca5b9659acba6b4bffa6f..0000000000000000000000000000000000000000
--- a/spaces/pkiage/time_series_decomposition_demo/test_environment.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import sys
-
-REQUIRED_PYTHON = "python3"
-
-
-def main():
- system_major = sys.version_info.major
- if REQUIRED_PYTHON == "python":
- required_major = 2
- elif REQUIRED_PYTHON == "python3":
- required_major = 3
- else:
- raise ValueError("Unrecognized python interpreter: {}".format(
- REQUIRED_PYTHON))
-
- if system_major != required_major:
- raise TypeError(
- "This project requires Python {}. Found: Python {}".format(
- required_major, sys.version))
- else:
- print(">>> Development environment passes all tests!")
-
-
-if __name__ == '__main__':
- main()
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/pygments/formatters/html.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/pygments/formatters/html.py
deleted file mode 100644
index 931d7c3fe294f889690263be26c6b75bc043bb2e..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/pygments/formatters/html.py
+++ /dev/null
@@ -1,989 +0,0 @@
-"""
- pygments.formatters.html
- ~~~~~~~~~~~~~~~~~~~~~~~~
-
- Formatter for HTML output.
-
- :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
- :license: BSD, see LICENSE for details.
-"""
-
-import functools
-import os
-import sys
-import os.path
-from io import StringIO
-
-from pip._vendor.pygments.formatter import Formatter
-from pip._vendor.pygments.token import Token, Text, STANDARD_TYPES
-from pip._vendor.pygments.util import get_bool_opt, get_int_opt, get_list_opt
-
-try:
- import ctags
-except ImportError:
- ctags = None
-
-__all__ = ['HtmlFormatter']
-
-
-_escape_html_table = {
- ord('&'): '&',
- ord('<'): '<',
- ord('>'): '>',
- ord('"'): '"',
- ord("'"): ''',
-}
-
-
-def escape_html(text, table=_escape_html_table):
- """Escape &, <, > as well as single and double quotes for HTML."""
- return text.translate(table)
-
-
-def webify(color):
- if color.startswith('calc') or color.startswith('var'):
- return color
- else:
- return '#' + color
-
-
-def _get_ttype_class(ttype):
- fname = STANDARD_TYPES.get(ttype)
- if fname:
- return fname
- aname = ''
- while fname is None:
- aname = '-' + ttype[-1] + aname
- ttype = ttype.parent
- fname = STANDARD_TYPES.get(ttype)
- return fname + aname
-
-
-CSSFILE_TEMPLATE = '''\
-/*
-generated by Pygments
-Copyright 2006-2023 by the Pygments team.
-Licensed under the BSD license, see LICENSE for details.
-*/
-%(styledefs)s
-'''
-
-DOC_HEADER = '''\
-
-
-
-
- %(title)s
-
-
-
-
-
-
-'''
-
-DOC_FOOTER = '''\
-
-
-'''
-
-
-class HtmlFormatter(Formatter):
- r"""
- Format tokens as HTML 4 ```` tags. By default, the content is enclosed
- in a ``
`` tag, itself wrapped in a ``
`` tag (but see the `nowrap` option).
- The ``
``'s CSS class can be set by the `cssclass` option.
-
- If the `linenos` option is set to ``"table"``, the ``
`` is
- additionally wrapped inside a ``
`` which has one row and two
- cells: one containing the line numbers and one containing the code.
- Example:
-
- .. sourcecode:: html
-
-
-
-
-
1
- 2
-
-
-
def foo(bar):
- pass
-
-
-
-
- (whitespace added to improve clarity).
-
- A list of lines can be specified using the `hl_lines` option to make these
- lines highlighted (as of Pygments 0.11).
-
- With the `full` option, a complete HTML 4 document is output, including
- the style definitions inside a ``
-
-
-
-
-
-
-
-
-
-
题目
-
-
-
-
答案
-
-
-
-
-
正误
-
-
-
-
得分
-
-
-
-
-
-
-
-
-
-
-
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/ Honkai Star Rail.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/ Honkai Star Rail.md
deleted file mode 100644
index 56ab346e55e359d1026276d0e927e239f8059f30..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/ Honkai Star Rail.md
+++ /dev/null
@@ -1,139 +0,0 @@
-
-
Honkai: Star Rail - A New Space Fantasy RPG
-
If you are a fan of space fantasy, adventure, and RPG games, you might want to check out Honkai: Star Rail, a new HoYoverse game that will take you on an epic journey across the galaxy. In this article, we will tell you everything you need to know about this game, including what it is, how to download and play it, and why you should give it a try.
-
What is Honkai: Star Rail?
-
Honkai: Star Rail is a new space fantasy RPG developed by miHoYo, the same company behind popular games like Genshin Impact and Honkai Impact 3rd. It is set in a vast galaxy where mysterious beings called Eons create and erase realities, leaving their marks on countless worlds. You play as a special traveler who inherits the will of the Trailblazer, an Eon who once explored the galaxy on a starship called the Astral Express. Along with your companions, you will board the Astral Express and follow the Trailblazer's path, discovering new civilizations, meeting new friends, and starting new adventures among various whimsical worlds. All the answers to your questions will be found among the stars. Are you ready to embark on this exciting journey?
The story of Honkai: Star Rail begins with you waking up on a deserted planet with no memory of who you are or where you came from. You find a device called a Stellaron, which allows you to communicate with an AI named Stella, who claims to be your guide. She tells you that you are a traveler who has been chosen by the Trailblazer, an Eon who left behind clues and coordinates for you to follow. She also tells you that you need to find the Astral Express, a starship that can take you across the galaxy.
-
As you explore the planet, you encounter various dangers and mysteries, as well as other travelers who have their own stories and motives. You also learn more about the Eons, powerful beings who can create and destroy realities with their will. Some Eons are benevolent and helpful, while others are hostile and destructive. You will have to face them as you travel through different worlds, each with its own unique culture, history, and environment.
-
The gameplay and features of the game
-
Honkai: Star Rail is a game that combines exploration, combat, and customization. You can explore various planets and regions with your Stellaron, which can transform into different modes depending on the terrain. You can also interact with various NPCs and objects, as well as collect resources and items that can help you in your journey.
-
The combat system of Honkai: Star Rail is fast-paced and dynamic. You can switch between different characters in your team, each with their own skills and abilities. You can also use your Stellaron to unleash powerful attacks or support your
allies. You can also customize your characters and Stellaron with different outfits, weapons, and accessories that can enhance their performance and appearance.
-
How to download and play Honkai: Star Rail?
-
If you are interested in playing Honkai: Star Rail, you will need to meet the following system requirements and compatibility:
-
The system requirements and compatibility of the game
-
-
-
Platform
-
Minimum Requirements
-
Recommended Requirements
-
-
-
Windows
-
OS: Windows 7 SP1 64-bit CPU: Intel Core i5-4460 or equivalent RAM: 8 GB GPU: NVIDIA GeForce GTX 960 or equivalent Storage: 30 GB
-
OS: Windows 10 64-bit CPU: Intel Core i7-6700K or equivalent RAM: 16 GB GPU: NVIDIA GeForce GTX 1060 or equivalent Storage: 50 GB
-
-
-
Android
-
OS: Android 6.0 or higher CPU: Qualcomm Snapdragon 845 or equivalent RAM: 4 GB Storage: 8 GB
-
OS: Android 9.0 or higher CPU: Qualcomm Snapdragon 865 or equivalent RAM: 8 GB Storage: 16 GB
-
-
-
iOS
-
OS: iOS 10.0 or higher Device: iPhone 6s or higher Storage: 8 GB
-
OS: iOS 13.0 or higher Device: iPhone X or higher Storage: 16 GB
-
-
-
The download links and steps for different platforms
-
To download and play Honkai: Star Rail, you can follow these steps for different platforms:
Tap on the "Get" button and wait for the game to download and install on your device.
-
Launch the game and log in with your miHoYo account or create a new one.
-
Enjoy the game!
-
-
-
The rewards and benefits for new players
-
If you are a new player of Honkai: Star Rail, you can enjoy some rewards and benefits that will help you start your adventure. Here are some of them:
-
-
You can get a free 5-star character of your choice from the first 10 pulls of the Star Rail banner.
-
You can get a free 4-star weapon of your choice from the first 10 pulls of the Weapon banner.
-
You can get a free Stellaron skin of your choice from the Stellaron Shop.
-
You can get various resources, items, and currencies from completing the tutorial, missions, and achievements.
-
You can get exclusive rewards from participating in the launch events and activities.
-
-
Why should you play Honkai: Star Rail?
-
Now that you know what Honkai: Star Rail is and how to download and play it, you might be wondering why you should play it. Well, there are many reasons why Honkai: Star Rail is a game worth playing, but here are some of the main ones:
-
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The stunning graphics and sound effects of the game
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Honkai: Star Rail is a game that will impress you with its beautiful graphics and sound effects. The game uses the Unreal Engine 4 to create realistic and detailed environments, characters, and animations. The game also features a dynamic day-night cycle, weather system, and lighting effects that add to the immersion and atmosphere of the game. The game also has an original soundtrack composed by HOYO-MiX, as well as voice acting by famous actors and actresses from China, Japan, Korea, and other regions. The game will make you feel like you are really traveling across the galaxy.
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The diverse and dynamic characters and events of the game
-
Honkai: Star Rail is a game that will surprise you with its diverse and dynamic characters and events. The game has over 20 characters that you can collect, each with their own personality, backstory, and skill set. You can also customize your characters with different outfits, weapons, and accessories that can change their appearance and abilities. The game also has various events that you can participate in, such as story quests, side quests, raids, dungeons, PvP battles, co-op missions, and more. The game will make you feel like you are part of a living and evolving galaxy.
The immersive and interactive exploration and combat of the game
-
Honkai: Star Rail is a game that will challenge you with its immersive and interactive exploration and combat. The game has a vast and open world that you can explore with your Stellaron, which can transform into different modes such as flight, land, and sea. You can also use your Stellaron to scan, hack, and manipulate various objects and devices that can help you in your exploration. The game also has a fluid and responsive combat system that allows you to switch between different characters and use their skills and combos. You can also use your Stellaron to support your team or unleash devastating attacks. The game will make you feel like you are in control of your own destiny.
-
Conclusion
-
Honkai: Star Rail is a new space fantasy RPG that will take you on an epic journey across the galaxy. You will discover new worlds, meet new friends, and start new adventures among various whimsical worlds. You will also learn more about the Eons, powerful beings who can create and destroy realities with their will. You will also enjoy the stunning graphics and sound effects, the diverse and dynamic characters and events, and the immersive and interactive exploration and combat of the game. Honkai: Star Rail is a game that you don't want to miss.
-
So what are you waiting for? Download Honkai: Star Rail today and join the millions of players who are already enjoying this amazing game. You will not regret it!
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FAQs
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What are Eons in Honkai: Star Rail?
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Eons are mysterious beings who can create and erase realities with their will. They are the source of the Honkai energy that flows through the galaxy. Some Eons are friendly and helpful, while others are hostile and destructive. You will encounter many Eons in your journey, some of whom will become your allies or enemies.
-
How many characters are there in Honkai: Star Rail?
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There are over 20 characters that you can collect in Honkai: Star Rail, each with their own personality, backstory, and skill set. Some of them are familiar faces from other HoYoverse games, such as Kiana Kaslana, Bronya Zaychik, Mei Raiden, Rita Rossweisse, Theresa Apocalypse, Fu Hua, Seele Vollerei, Durandal, Rosalia Olenyeva, Liliya Olenyeva, Himeko Murata, Yae Sakura, Kallen Kaslana, Cecilia Schariac, Welt Yang, Kevin Kaslana, Otto Apocalypse, Sirin, Ana Schariac, and Cocolia. Some of them are new characters that are exclusive to Honkai: Star Rail, such as Stella, Lumiya, Riaa, Naya, Luka, Aria, Elisea, and more. You can unlock more characters by completing quests, participating in events, or using the gacha system.
-
What are Stellaron and Astral Express in Honkai: Star Rail?
-
Stellaron is a device that allows you to communicate with Stella, your guide and AI companion. It can also transform into different modes that can help you in your exploration and combat, such as flight, land, and sea. You can also customize your Stellaron with different skins and accessories that can change its appearance and functions.
-
Astral Express is a starship that can take you across the galaxy. It was once used by the Trailblazer, an Eon who left behind clues and coordinates for you to follow. You can use the Astral Express to travel between different planets and regions, as well as access various facilities and services, such as the bridge, the hangar, the dormitory, the shop, the workshop, the lounge, and more. You can also decorate and upgrade your Astral Express with different items and modules that can improve its performance and appearance.
-
How can I get more warps in Honkai: Star Rail?
-
Warps are a currency that you can use to perform gacha pulls in Honkai: Star Rail. You can get more warps by completing quests, achievements, and events, as well as logging in daily, topping up, or exchanging other currencies. You can also get more warps by using the warp generator, a feature that allows you to generate warps by consuming energy. You can get energy by playing the game or using real money.
-
Is Honkai: Star Rail connected to other HoYoverse games?
-
Honkai: Star Rail is connected to other HoYoverse games, such as Genshin Impact and Honkai Impact 3rd. The game shares some of the characters, lore, and elements from these games, as well as some of the gameplay mechanics and features. However, Honkai: Star Rail is also a standalone game that has its own story, setting, and style. You do not need to play other HoYoverse games to enjoy Honkai: Star Rail.
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How to Download 12th Admit Card for Board Exams 2023
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If you are a student who is going to appear for the class 12th board exams in 2023, then you must be wondering how to download your admit card. An admit card is a document that contains your personal details, exam details, and exam center details. It is mandatory to carry your admit card to the exam hall, otherwise you will not be allowed to enter. Without an admit card, you will not be able to write your exam.
In India, there are different boards that conduct the class 12th exams, such as CBSE, CISCE, and various state boards. Each board has its own procedure and timeline to release the admit card for the students. In this article, we will tell you how to download the admit card for each board, what details are mentioned on it, and what instructions you need to follow before and during the exam.
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CBSE 12th Admit Card 2023
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The Central Board of Secondary Education (CBSE) is one of the most popular boards in India. It conducts the class 12th exams in February-March every year. The CBSE releases the admit card for the regular and private candidates on its official website - cbse.gov.in. Here are the steps to download the CBSE 12th admit card for both types of candidates.
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How to download CBSE 12th admit card for regular candidates?
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The regular candidates are those who are enrolled in a school affiliated to CBSE. They can get their admit card from their respective schools. The schools can download the admit card from the CBSE website by following these steps:
-
-
Visit the official CBSE website - cbse.gov.in.
-
Click on the e-PAREEKSHA tab and then click on the link that says, 'Admit Card/Centre Material for Examination 2021-2022' option.
-
Enter school's User Id, Password and Security Pin to login.
-
Download CBSE Term 2 admit card data on your system.
-
Print it and put school's stamp and signature on the space provided.
-
Handover the copy to the concerned students.
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-
How to download CBSE 12th admit card for private candidates?
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The private candidates are those who are not enrolled in any school affiliated to CBSE. They can download their admit card from the CBSE website by following these steps:
-
-
Visit the official CBSE website - cbse.gov.in.
-
Click on the link "Admit Card for Private Candidates Main Examination 2024" under the "In Focus" section.
-
Select the option by which you want to search for your admit card, such as Application No., Previous Roll No. and Year, or Candidate Name.
-
Enter the required details and click on the "Proceed" button.
-
Download and print your CBSE 12th admit card for private candidates.
-
-
Details mentioned on CBSE 12th admit card
-
The CBSE 12th admit card contains the following details:
-
How to download CBSE class 12 admit card 2023
-How to download 12th board exam hall ticket 2023
-How to download class 12 admit card from CBSE website
-How to download CBSE 12th term 2 admit card 2023
-How to download class 12 admit card for private candidates
-How to download CBSE class 12 admit card without login
-How to download class 12 admit card for compartment exam
-How to download CBSE class 12 admit card with roll number
-How to download class 12 admit card for practical exam
-How to download CBSE class 12 admit card for improvement exam
-How to download class 12 admit card for revaluation exam
-How to download CBSE class 12 admit card for supplementary exam
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-How to download class 12 admit card for guidelines and instructions
-How to download CBSE class 12 admit card for FAQs and helpline number
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Student's name, roll number, date of birth, and photograph.
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School's name and code.
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Exam center's name, code, and address.
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Subject names, codes, and dates.
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Signature of the student and the principal.
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Important instructions for the exam.
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-
Important instructions for CBSE 12th exam
-
The students who are appearing for the CBSE 12th exam must follow these instructions:
-
-
Check the admit card carefully and report any discrepancy to the school or CBSE immediately.
-
Carefully read the COVID-19 guidelines issued by CBSE and follow them strictly.
-
Reach the exam center at least 30 minutes before the exam time.
-
Carry your admit card, school ID card, and other necessary items to the exam hall.
-
Do not carry any prohibited items such as mobile phones, calculators, books, etc. to the exam hall.
-
Follow the instructions given by the invigilator and maintain discipline during the exam.
-
-
CISCE 12th Admit Card 2023
-
The Council for the Indian School Certificate Examinations (CISCE) is another board that conducts the class 12th exams in India. It is also known as ISC board. It conducts the class 12th exams in February-March every year. The CISCE releases the admit card for the regular and private candidates on its official website - cisce.org. Here are the steps to download the CISCE 12th admit card for both types of candidates.
-
How to download CISCE 12th admit card for regular candidates?
-
The regular candidates are those who are enrolled in a school affiliated to CISCE. They can get their admit card from their respective schools. The schools can download the admit card from the CISCE website by following these steps:
-
-
Visit the official CISCE website - cisce.org.
-
Click on the "Services" tab and then click on "Downloads".
-
Select "ISC" from the drop-down menu and then click on "Admit Card".
-
Enter school's User Id and Password to login.
-
Select "Admit Card" from the left menu and then select "Print Admit Card".
-
Select "All Candidates" or "Individual Candidate" as per your preference.
-
Download and print the admit card for each candidate.
-
How to download CISCE 12th admit card for private candidates?
-
The private candidates are those who are not enrolled in any school affiliated to CISCE. They can download their admit card from the CISCE website by following these steps:
-
-
Visit the official CISCE website - cisce.org.
-
Click on the "Services" tab and then click on "Downloads".
-
Select "ISC" from the drop-down menu and then click on "Admit Card".
-
Enter your Unique ID, Index Number, and Captcha code to login.
-
Download and print your CISCE 12th admit card for private candidates.
-
-
Details mentioned on CISCE 12th admit card
-
The CISCE 12th admit card contains the following details:
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-
Student's name, roll number, date of birth, and photograph.
-
School's name and code.
-
Exam center's name, code, and address.
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Subject names, codes, and dates.
-
Signature of the student and the principal.
-
Important instructions for the exam.
-
-
Important instructions for CISCE 12th exam
-
The students who are appearing for the CISCE 12th exam must follow these instructions:
-
-
Check the admit card carefully and report any discrepancy to the school or CISCE immediately.
-
Carefully read the COVID-19 guidelines issued by CISCE and follow them strictly.
-
Reach the exam center at least 15 minutes before the exam time.
-
Carry your admit card, school ID card, and other necessary items to the exam hall.
-
Do not carry any prohibited items such as mobile phones, calculators, books, etc. to the exam hall.
-
Follow the instructions given by the invigilator and maintain discipline during the exam.
-
-
State Board 12th Admit Card 2023
-
Besides CBSE and CISCE, there are various state boards that conduct the class 12th exams in India. Some of the major state boards are UP Board, Maharashtra Board, Bihar Board, Tamil Nadu Board, Karnataka Board, etc. Each state board has its own procedure and timeline to release the admit card for the students. The students can download their admit card from the respective state board websites by following these steps:
Click on "II PUC March 2024 Examination Admission Ticket" under "Examination".
Select your district, college, and stream from the drop-down menus.
Enter your registration number and click on "Get Admission Ticket".
Download and print your Karnataka Board 12th admit card.
-
-
Details mentioned on state board 12th admit card
-
The state board 12th admit card contains the following details:
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-
Student's name, roll number, date of birth, and photograph.
-
School's name and code.
-
Exam center's name, code, and address.
-
Subject names, codes, and dates.
-
Signature of the student and the principal.
-
Important instructions for the exam.
-
-
Important instructions for state board 12th exam
-
The students who are appearing for the state board 12th exam must follow these instructions:
-
-
Check the admit card carefully and report any discrepancy to the school or state board immediately.
-
Carefully read the COVID-19 guidelines issued by the state board and follow them strictly.
-
Reach the exam center at least 15 minutes before the exam time.
-
Carry your admit card, school ID card, and other necessary items to the exam hall.
-
Do not carry any prohibited items such as mobile phones, calculators, books, etc. to the exam hall.
-
Follow the instructions given by the invigilator and maintain discipline during the exam.
-
-
Conclusion
-
In this article, we have explained how to download the 12th admit card for board exams 2023 for different boards in India. The admit card is a crucial document that you must carry to the exam hall. It contains your personal details, exam details, and exam center details. You can download your admit card from the respective board websites by following the steps given above. You should also check the details mentioned on your admit card and follow the important instructions for the exam.
-
To prepare well for the board exams, you should revise your syllabus thoroughly, practice previous year papers and sample papers, manage your time effectively, and stay calm and confident. We wish you all the best for your board exams!
-
FAQs
-
Here are some common FAQs related to the 12th admit card:
-
-
Q: When will the 12th admit card be released?
-
A: The 12th admit card will be released by different boards at different times. Generally, it is released one or two months before the exam date. You should check the official website of your board regularly for any updates.
-
Q: What if I lose my 12th admit card?
-
A: If you lose your 12th admit card, you should contact your school or board immediately and request for a duplicate copy. You may have to pay a nominal fee and submit some documents to get a duplicate copy. You should also file a police complaint in case of theft or loss of your admit card.
-
Q: Can I change my exam center after getting my 12th admit card?
-
A: No, you cannot change your exam center after getting your 12th admit card. The exam center allotted to you is final and binding. You should visit your exam center before the exam day to avoid any confusion or inconvenience.
-
Q: How can I correct any mistake in my 12th admit card?
-
A: If you find any mistake in your 12th admit card, such as spelling error, wrong photograph, incorrect subject code, etc., you should report it to your school or board immediately. You may have to submit some proof of identity and documents to get it corrected before the exam. You should not ignore any mistake in your admit card as it may cause trouble during the exam.
-
Q: What are the documents required to download the 12th admit card?
-
A: The documents required to download the 12th admit card vary depending on the board and the type of candidate. Generally, you may need your school code, registration number, application number, date of birth, name, etc. to download your admit card. You should keep these details handy while downloading your admit card.
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diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Galaxy Attack Premium How to Unlock the MOD Menu and Damage Mode.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Galaxy Attack Premium How to Unlock the MOD Menu and Damage Mode.md
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Galaxy Attack Premium Mod Apk: A Guide for Space Shooter Fans
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If you are a fan of arcade shooting games like Galaga, Space Invaders, or Asteroids, you might want to check out Galaxy Attack Premium Mod Apk, a fun and addictive game that will challenge your reflexes and skills. In this game, you will take control of a spaceship and fight against waves of alien invaders who want to destroy the Earth and the galaxy. You will also be able to upgrade your spaceship, use special skills, and enjoy stunning graphics and sound effects.
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But what if you want to enjoy the game without any limitations or restrictions? What if you want to have unlimited money, VIP level 12, and access to all the ships and weapons in the game? Well, that's where Galaxy Attack Premium Mod Apk comes in handy. This is a modified version of the original game that gives you all these benefits and more. In this article, we will show you how to download and install Galaxy Attack Premium Mod Apk, how to play it, and some tips and tricks to help you save the galaxy from alien attack.
Galaxy Attack Premium Mod Apk is based on Galaxy Attack, a popular arcade shooting game developed by OneSoft Global PTE. LTD. The game has over 100 million downloads on Google Play Store and has received positive reviews from players and critics alike. The game features:
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Multiplayer mode: You can play with or against other players online in 1 vs 1 or 1 vs 3 modes.
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High-quality images: The game has stunning graphics that are optimized for tablets and large screens.
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Active skills: You can use different skills during space battles, such as missiles, lasers, shields, bombs, etc.
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Many levels: The game has over 160 levels on various difficulties, each with different enemies, bosses, and challenges.
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Many ships and weapons: You can choose from different ships with different attributes and abilities, as well as upgrade your guns and lasers.
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-
The benefits of using the mod apk version
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Galaxy Attack Premium Mod Apk is a modified version of the original game that gives you some extra advantages that will make your gameplay more enjoyable and easier. Some of these advantages are:
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Unlimited money: You will have unlimited gold and crystals that you can use to buy or upgrade anything in the game.
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VIP level 12: You will have the highest VIP level in the game, which will give you access to exclusive features and rewards.
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Unlocked shopping menu: You will be able to buy any ship or weapon in the game without any restrictions or requirements.
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No ads: You will not see any ads or pop-ups in the game, which will make your gaming experience smoother and more enjoyable.
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How to Download and Install Galaxy Attack Premium Mod Apk?
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The steps to download the mod apk file from a reliable source
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If you want to download and install Galaxy Attack Premium Mod Apk, you need to follow these simple steps:
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Go to this link and click on the download button to get the mod apk file.
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Wait for the download to finish and then locate the file on your device.
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Before installing the file, make sure you have enabled the "Unknown Sources" option in your device settings. This will allow you to install apps from sources other than the Google Play Store.
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The instructions to install the mod apk file on your device
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Once you have downloaded and enabled the "Unknown Sources" option, you can proceed to install Galaxy Attack Premium Mod Apk by following these instructions:
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Tap on the mod apk file and select "Install".
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Wait for the installation to complete and then open the game.
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Enjoy playing Galaxy Attack Premium Mod Apk with unlimited money, VIP level 12, and unlocked shopping menu.
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How to Play Galaxy Attack Premium Mod Apk?
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The basic gameplay and controls of the game
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Galaxy Attack Premium Mod Apk is very easy to play and control. The game is based on touch screen gestures, so you don't need any buttons or joysticks. Here are the basic gameplay and controls of the game:
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To move your spaceship, you just need to drag your finger on the screen. You can move it left, right, up, or down to avoid enemy fire and collect power-ups.
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To shoot your guns or lasers, you just need to tap on the screen. You can also hold your finger on the screen to activate auto-fire mode.
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To use your skills, you just need to tap on the icons at the bottom of the screen. You can use missiles, lasers, shields, bombs, etc. depending on your ship and weapon.
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To pause or resume the game, you just need to tap on the pause button at the top right corner of the screen. You can also access the settings, sound, and music options from there.
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The tips and tricks to improve your skills and score
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If you want to become a better space shooter and get higher scores in Galaxy Attack Premium Mod Apk, you might want to follow these tips and tricks:
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Upgrade your ship and weapon regularly. You can use your unlimited money to buy or upgrade any ship or weapon in the game. This will increase your damage, speed, health, and skills.
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Choose the right ship and weapon for each level. Different ships and weapons have different attributes and abilities that suit different situations. For example, some ships are faster but weaker, while some weapons are more powerful but slower.
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Avoid getting hit by enemy fire or asteroids. This will reduce your health and make you lose lives. You can use your shield skill or dodge them by moving your spaceship.
-
Collect power-ups and bonuses. These are items that appear randomly on the screen and give you extra benefits such as health, coins, crystals, weapons, skills, etc.
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Play multiplayer mode with or against other players. This will help you improve your skills and compete with others. You can also earn more rewards and achievements by playing multiplayer mode.
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-
Conclusion
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A summary of the main points and a call to action
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Galaxy Attack Premium Mod Apk is a great game for anyone who loves arcade shooting games like Galaga, Space Invaders, or Asteroids. It has amazing graphics, sound effects, gameplay, and features that will keep you entertained for hours. It also has a mod apk version that gives you unlimited money, VIP level 12, and unlocked shopping menu that will make your gaming experience more enjoyable and easier.
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diff --git a/spaces/skf15963/summary/fengshen/models/bart/modeling_bart.py b/spaces/skf15963/summary/fengshen/models/bart/modeling_bart.py
deleted file mode 100644
index f9a58ac8036fbc0bb9334b083b12a5599950d355..0000000000000000000000000000000000000000
--- a/spaces/skf15963/summary/fengshen/models/bart/modeling_bart.py
+++ /dev/null
@@ -1,423 +0,0 @@
-import warnings
-from pytorch_lightning import LightningModule
-from fengshen.models import transformer_utils
-
-import torch
-import torch.utils.checkpoint
-from torch import nn
-import torch.nn.functional as F
-
-from dataclasses import dataclass
-from typing import Optional, Tuple
-
-from transformers.file_utils import *
-from transformers.modeling_outputs import *
-from transformers.models.bart import *
-from transformers.models.bart.modeling_bart import BartClassificationHead
-
-
-_CONFIG_FOR_DOC = "BartConfig"
-
-
-# ------------------------ ZZ: CBart addition ------------------------
-
-
-def _reorder_buffer(attn_cache, new_order):
- for k, input_buffer_k in attn_cache.items():
- if input_buffer_k is not None:
- attn_cache[k] = input_buffer_k.index_select(0, new_order)
- return attn_cache
-
-
-def _make_linear_from_emb(emb):
- vocab_size, emb_size = emb.weight.shape
- lin_layer = nn.Linear(vocab_size, emb_size, bias=False)
- lin_layer.weight.data = emb.weight.data
- return lin_layer
-
-
-BART_GENERATION_EXAMPLE = r"""
- Summarization example::
-
- >>> from transformers import BartTokenizer, BartForConditionalGeneration, BartConfig
-
- >>> model = BartForConditionalGeneration.from_pretrained('facebook/bart-large-cnn')
- >>> tokenizer = BartTokenizer.from_pretrained('facebook/bart-large-cnn')
-
- >>> ARTICLE_TO_SUMMARIZE = "My friends are cool but they eat too many carbs."
- >>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors='pt')
-
- >>> # Generate Summary
- >>> summary_ids = model.generate(inputs['input_ids'], num_beams=4, max_length=5, early_stopping=True)
- >>> print([tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids])
-
- Mask filling example::
-
- >>> from transformers import BartTokenizer, BartForConditionalGeneration
- >>> tokenizer = BartTokenizer.from_pretrained('facebook/bart-large')
- >>> TXT = "My friends are but they eat too many carbs."
-
- >>> model = BartForConditionalGeneration.from_pretrained('facebook/bart-large')
- >>> input_ids = tokenizer([TXT], return_tensors='pt')['input_ids']
- >>> logits = model(input_ids).logits
-
- >>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
- >>> probs = logits[0, masked_index].softmax(dim=0)
- >>> values, predictions = probs.topk(5)
-
- >>> tokenizer.decode(predictions).split()
-"""
-
-
-@dataclass
-class CBartLMOutput(ModelOutput):
- """
- Base class for CBart specific language models outputs.
-
- Args:
- ....
- """
- loss: Optional[torch.FloatTensor] = None
- encoder_loss: Optional[torch.FloatTensor] = None
- decoder_loss: Optional[torch.FloatTensor] = None
- encoder_logits: torch.FloatTensor = None
- logits: torch.FloatTensor = None
- past_key_values: Optional[Tuple[torch.FloatTensor]] = None
- decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
- decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
- encoder_last_hidden_state: Optional[torch.FloatTensor] = None
- encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
- encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
-
-
-class BartForTextInfill(BartPretrainedModel):
- """
- this class is designed for text infilling.
- During training, the encoder is used to predict replace, insert,
- and the decoder is used to generate original input.
- Compared with BartForConditionalGeneration class,
- we add a module over the encoder and add a new loss for the encoder.
- """
- base_model_prefix = "model"
- authorized_missing_keys = [r"final_logits_bias",
- r"encoder\.version", r"decoder\.version"]
-
- def __init__(self, config: BartConfig):
- super().__init__(config)
- base_model = BartModel(config)
- self.model = base_model
- self.register_buffer("final_logits_bias", torch.zeros(
- (1, self.model.shared.num_embeddings)))
- # print( config.encoder_loss_type, config.num_labels)
-
- # add a new attribute into BartConfig class (revise BartConfig)
- self.encoder_loss_type = config.encoder_loss_type
- self.num_labels = config.num_labels
- if self.encoder_loss_type == 0: # 0 is classification loss, 1 is regression loss
- # add a classification module for the encoder
- self.classification_head = BartClassificationHead(
- config.d_model, config.d_model, config.num_labels, config.classif_dropout,
- )
- else:
- # add a regression module for the encoder
- self.classification_head = BartClassificationHead(
- config.d_model, config.d_model, 1, config.classif_dropout,
- )
-
- self.model._init_weights(self.classification_head.dense)
- self.model._init_weights(self.classification_head.out_proj)
- self.loss_weight = config.loss_weight
- self.register_buffer("label_weights", torch.zeros((self.num_labels)))
-
- def resize_token_embeddings(self, new_num_tokens: int) -> nn.Embedding:
- old_num_tokens = self.model.shared.num_embeddings
- new_embeddings = super().resize_token_embeddings(new_num_tokens)
- self.model.shared = new_embeddings
- self._resize_final_logits_bias(new_num_tokens, old_num_tokens)
- return new_embeddings
-
- def _resize_final_logits_bias(self, new_num_tokens: int, old_num_tokens: int) -> None:
- if new_num_tokens <= old_num_tokens:
- new_bias = self.final_logits_bias[:, :new_num_tokens]
- else:
- extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens),
- device=self.final_logits_bias.device)
- new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1)
- self.register_buffer("final_logits_bias", new_bias)
-
- @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
- @add_end_docstrings(BART_GENERATION_EXAMPLE)
- def forward(
- self,
- input_ids,
- attention_mask=None,
- encoder_outputs=None,
- decoder_input_ids=None,
- decoder_attention_mask=None,
- past_key_values=None,
- encoder_labels=None,
- labels=None,
- use_cache=None,
- output_attentions=None,
- output_hidden_states=None,
- return_dict=True,
- **unused,
- ):
- r"""
- labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
- Labels for computing the masked language modeling loss.
- Indices should either be in ``[0, ..., config.vocab_size]`` or -100 (see ``input_ids`` docstring).
- Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens
- with labels in ``[0, ..., config.vocab_size]``.
-
- Returns:
-
- Conditional generation example::
-
- # Mask filling only works for bart-large
- from transformers import BartTokenizer, BartForConditionalGeneration
- tokenizer = BartTokenizer.from_pretrained('facebook/bart-large')
- TXT = "My friends are but they eat too many carbs."
-
- model = BartForConditionalGeneration.from_pretrained('facebook/bart-large')
- input_ids = tokenizer([TXT], return_tensors='pt')['input_ids']
- logits = model(input_ids).logits
-
- masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
- probs = logits[0, masked_index].softmax(dim=0)
- values, predictions = probs.topk(5)
-
- tokenizer.decode(predictions).split()
- # ['good', 'great', 'all', 'really', 'very']
- """
- if "lm_labels" in unused:
- warnings.warn(
- "The `lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
- FutureWarning,
- )
- labels = unused.pop("lm_labels")
- if "decoder_cached_states" in unused:
- warnings.warn(
- "The `decoder_cached_states` argument is deprecated and will be removed in a future version, use `decoder_past_key_values` instead.",
- FutureWarning,
- )
- decoder_past_key_values = unused.pop("decoder_cached_states")
- return_dict = return_dict if return_dict is not None else False
-
- if labels is not None:
- use_cache = False
-
- outputs = self.model(
- input_ids,
- attention_mask=attention_mask,
- decoder_input_ids=decoder_input_ids,
- encoder_outputs=encoder_outputs,
- decoder_attention_mask=decoder_attention_mask,
- past_key_values=past_key_values,
- use_cache=use_cache,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- )
-
- # logits and loss for the encoder
- # last hidden state
- encoder_last_hidden_state = outputs['encoder_last_hidden_state']
- # eos_mask = input_ids.eq(self.config.eos_token_id)
- # if len(torch.unique(eos_mask.sum(1))) > 1:
- # raise ValueError("All examples must have the same number of tokens.")
- # sentence_representation = x[eos_mask, :].view(x.size(0), -1, x.size(-1))[:, -1, :]
- encoder_logits = self.classification_head(encoder_last_hidden_state)
- encoder_loss = None
- if encoder_labels is not None:
- # classification loss
- if self.encoder_loss_type == 0:
- # ZZ: seems like MSE loss does not support weighting, so only CEL has weighting applied for now
- loss_fct = nn.CrossEntropyLoss(weight=self.label_weights)
- encoder_loss = loss_fct(
- encoder_logits.view(-1, self.config.num_labels), encoder_labels.view(-1))
- # regression loss
- else:
- encoder_logits = encoder_logits.view(
- encoder_logits.size(0), -1)
- encoder_logits = torch.sigmoid(
- encoder_logits) * self.num_labels - 0.5
- loss_fct = nn.MSELoss(reduction='none')
- _loss = loss_fct(encoder_logits, encoder_labels)
- encoder_loss = torch.mean(_loss[encoder_labels >= 0])
- # encoder_loss =_loss[encoder_labels>=0]
-
- # logits and loss for the decoder
- lm_logits = F.linear(
- outputs[0], self.model.shared.weight, bias=self.final_logits_bias)
- masked_lm_loss = None
- if labels is not None:
- loss_fct = nn.CrossEntropyLoss()
- # TODO(SS): do we need to ignore pad tokens in labels?
- masked_lm_loss = loss_fct(
- lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
-
- loss = None
- if masked_lm_loss is not None and encoder_loss is not None:
- loss = encoder_loss * self.loss_weight + masked_lm_loss
-
- if not return_dict:
- output = (lm_logits,) + outputs[1:]
- return ((loss,) + output) if loss is not None else output
-
- return CBartLMOutput(
- loss=loss,
- encoder_loss=encoder_loss,
- decoder_loss=masked_lm_loss,
- encoder_logits=encoder_logits,
- logits=lm_logits,
- past_key_values=outputs.past_key_values,
- decoder_hidden_states=outputs.decoder_hidden_states,
- decoder_attentions=outputs.decoder_attentions,
- encoder_last_hidden_state=outputs.encoder_last_hidden_state,
- encoder_hidden_states=outputs.encoder_hidden_states,
- encoder_attentions=outputs.encoder_attentions,
- )
-
- def prepare_inputs_for_generation(self, decoder_input_ids, past, attention_mask, use_cache, **kwargs):
- assert past is not None, "past has to be defined for encoder_outputs"
-
- encoder_outputs, past_key_values = past
- return {
- "input_ids": None, # encoder_outputs is defined. input_ids not needed
- "encoder_outputs": encoder_outputs,
- "past_key_values": past_key_values,
- "decoder_input_ids": decoder_input_ids,
- "attention_mask": attention_mask,
- # change this to avoid caching (presumably for debugging)
- "use_cache": use_cache,
- }
-
- def adjust_logits_during_generation(self, logits, cur_len, max_length):
- if cur_len == 1:
- self._force_token_ids_generation(logits, self.config.bos_token_id)
- if cur_len == max_length - 1 and self.config.eos_token_id is not None:
- self._force_token_ids_generation(logits, self.config.eos_token_id)
- return logits
-
- def _force_token_ids_generation(self, scores, token_ids) -> None:
- """force one of token_ids to be generated by setting prob of all other tokens to 0"""
- if isinstance(token_ids, int):
- token_ids = [token_ids]
- all_but_token_ids_mask = torch.tensor(
- [x for x in range(self.config.vocab_size) if x not in token_ids],
- dtype=torch.long,
- device=next(self.parameters()).device,
- )
- assert len(
- scores.shape) == 2, "scores should be of rank 2 with shape: [batch_size, vocab_size]"
- scores[:, all_but_token_ids_mask] = -float("inf")
-
- @staticmethod
- def _reorder_cache(past, beam_idx):
- ((enc_out, enc_mask), past_key_values) = past
- reordered_past = []
- for layer_past in past_key_values:
- # get the correct batch idx from decoder layer's batch dim for cross and self-attn
- layer_past_new = {
- attn_key: _reorder_buffer(attn_cache, beam_idx) for attn_key, attn_cache in layer_past.items()
- }
- reordered_past.append(layer_past_new)
-
- new_enc_out = enc_out if enc_out is None else enc_out.index_select(
- 0, beam_idx)
- new_enc_mask = enc_mask if enc_mask is None else enc_mask.index_select(
- 0, beam_idx)
-
- past = ((new_enc_out, new_enc_mask), reordered_past)
- return past
-
- def get_encoder(self):
- return self.model.encoder
-
- def get_output_embeddings(self):
- return _make_linear_from_emb(self.model.shared) # make it on the fly
-
- def get_encoder_logits(self, input_ids, attention_mask=None):
- # print(input_ids, attention_mask)
- # encoder_outputs = self.model.get_encoder_outputs(
- # self,
- # input_ids,
- # attention_mask=attention_mask,
- # output_attentions=None,
- # output_hidden_states=None,
- # return_dict=None,
- # )
-
- encoder_outputs = self.model.encoder(
- input_ids=input_ids,
- attention_mask=attention_mask,
- return_dict=True
- )
- # logits and loss for the encoder
- # last hidden state
- encoder_last_hidden_state = encoder_outputs['last_hidden_state']
- encoder_logits = self.classification_head(encoder_last_hidden_state)
-
- # classification
- if self.encoder_loss_type == 0:
- # probs = torch.softmax(encoder_logits,dim=-1)
- pass
- # regression
- else:
- encoder_logits = encoder_logits.view(encoder_logits.size(0), -1)
- encoder_logits = torch.sigmoid(
- encoder_logits) * self.num_labels - 0.5
- return encoder_outputs, encoder_logits
-
-
-class CBartLightning(LightningModule):
- @staticmethod
- def add_module_specific_args(parent_args):
- parser = parent_args.add_argument_group("CBart specific parameters")
- parser.add_argument('--num_labels', type=int, default=3)
- parser.add_argument('--encoder_loss_type', type=int, default=0)
- parser.add_argument('--loss_weight', type=float, default=1.0)
- parser.add_argument('--label_weights', type=float, nargs='+', default=[1.0, 1.0, 1.0])
- parser.add_argument('--masked_lm', type=float, default=0)
- return parent_args
-
- def __init__(
- self,
- args,
- **kwargs,
- ):
- super().__init__()
- self.save_hyperparameters(args)
- self.model = BartForTextInfill.from_pretrained(args.model_path, num_labels=self.hparams.num_labels,
- encoder_loss_type=self.hparams.encoder_loss_type,
- loss_weight=self.hparams.loss_weight,)
- self.model.label_weights = torch.tensor(
- self.hparams.label_weights, dtype=torch.half)
-
- def forward(self, **inputs):
- return self.model(**inputs)
-
- def training_step(self, batch, batch_idx):
- outputs = self(**batch)
- return outputs
-
- def validation_step(self, batch, batch_idx, dataloader_idx=0):
- outputs = self(**batch)
- val_loss = outputs["loss"]
-
- return {"loss": val_loss}
-
- def setup(self, stage=None) -> None:
- if stage != "fit":
- return
- # Get dataloader by calling it - train_dataloader() is called after setup() by default
- train_loader = self.trainer._data_connector._train_dataloader_source.dataloader()
-
- # Calculate total steps
- tb_size = self.hparams.train_batchsize * max(1, self.trainer.gpus)
- ab_size = self.trainer.accumulate_grad_batches * float(self.trainer.max_epochs)
- self.total_steps = (len(train_loader.dataset) // tb_size) // ab_size
-
- def configure_optimizers(self):
- transformer_utils.configure_optimizers(self)
diff --git a/spaces/skytnt/moe-tts/export_model.py b/spaces/skytnt/moe-tts/export_model.py
deleted file mode 100644
index c302aebd8ac181237377489b4732680ec13e4b31..0000000000000000000000000000000000000000
--- a/spaces/skytnt/moe-tts/export_model.py
+++ /dev/null
@@ -1,13 +0,0 @@
-import torch
-
-if __name__ == '__main__':
- model_path = "saved_model/19/model.pth"
- output_path = "saved_model/19/model1.pth"
- checkpoint_dict = torch.load(model_path, map_location='cpu')
- checkpoint_dict_new = {}
- for k, v in checkpoint_dict.items():
- if k == "optimizer":
- print("remove optimizer")
- continue
- checkpoint_dict_new[k] = v
- torch.save(checkpoint_dict_new, output_path)
diff --git a/spaces/smf2010/ysfj/README.md b/spaces/smf2010/ysfj/README.md
deleted file mode 100644
index 2b5ed8742f81c7e4cb59a3469e41a7860766d28f..0000000000000000000000000000000000000000
--- a/spaces/smf2010/ysfj/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: ysfj
-emoji: 👁
-colorFrom: red
-colorTo: blue
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/songdaooi/Swap/face_parsing/resnet.py b/spaces/songdaooi/Swap/face_parsing/resnet.py
deleted file mode 100644
index aa2bf95130e9815ba378cb6f73207068b81a04b9..0000000000000000000000000000000000000000
--- a/spaces/songdaooi/Swap/face_parsing/resnet.py
+++ /dev/null
@@ -1,109 +0,0 @@
-#!/usr/bin/python
-# -*- encoding: utf-8 -*-
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.model_zoo as modelzoo
-
-# from modules.bn import InPlaceABNSync as BatchNorm2d
-
-resnet18_url = 'https://download.pytorch.org/models/resnet18-5c106cde.pth'
-
-
-def conv3x3(in_planes, out_planes, stride=1):
- """3x3 convolution with padding"""
- return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
- padding=1, bias=False)
-
-
-class BasicBlock(nn.Module):
- def __init__(self, in_chan, out_chan, stride=1):
- super(BasicBlock, self).__init__()
- self.conv1 = conv3x3(in_chan, out_chan, stride)
- self.bn1 = nn.BatchNorm2d(out_chan)
- self.conv2 = conv3x3(out_chan, out_chan)
- self.bn2 = nn.BatchNorm2d(out_chan)
- self.relu = nn.ReLU(inplace=True)
- self.downsample = None
- if in_chan != out_chan or stride != 1:
- self.downsample = nn.Sequential(
- nn.Conv2d(in_chan, out_chan,
- kernel_size=1, stride=stride, bias=False),
- nn.BatchNorm2d(out_chan),
- )
-
- def forward(self, x):
- residual = self.conv1(x)
- residual = F.relu(self.bn1(residual))
- residual = self.conv2(residual)
- residual = self.bn2(residual)
-
- shortcut = x
- if self.downsample is not None:
- shortcut = self.downsample(x)
-
- out = shortcut + residual
- out = self.relu(out)
- return out
-
-
-def create_layer_basic(in_chan, out_chan, bnum, stride=1):
- layers = [BasicBlock(in_chan, out_chan, stride=stride)]
- for i in range(bnum-1):
- layers.append(BasicBlock(out_chan, out_chan, stride=1))
- return nn.Sequential(*layers)
-
-
-class Resnet18(nn.Module):
- def __init__(self):
- super(Resnet18, self).__init__()
- self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
- bias=False)
- self.bn1 = nn.BatchNorm2d(64)
- self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
- self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1)
- self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2)
- self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2)
- self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2)
- self.init_weight()
-
- def forward(self, x):
- x = self.conv1(x)
- x = F.relu(self.bn1(x))
- x = self.maxpool(x)
-
- x = self.layer1(x)
- feat8 = self.layer2(x) # 1/8
- feat16 = self.layer3(feat8) # 1/16
- feat32 = self.layer4(feat16) # 1/32
- return feat8, feat16, feat32
-
- def init_weight(self):
- state_dict = modelzoo.load_url(resnet18_url)
- self_state_dict = self.state_dict()
- for k, v in state_dict.items():
- if 'fc' in k: continue
- self_state_dict.update({k: v})
- self.load_state_dict(self_state_dict)
-
- def get_params(self):
- wd_params, nowd_params = [], []
- for name, module in self.named_modules():
- if isinstance(module, (nn.Linear, nn.Conv2d)):
- wd_params.append(module.weight)
- if not module.bias is None:
- nowd_params.append(module.bias)
- elif isinstance(module, nn.BatchNorm2d):
- nowd_params += list(module.parameters())
- return wd_params, nowd_params
-
-
-if __name__ == "__main__":
- net = Resnet18()
- x = torch.randn(16, 3, 224, 224)
- out = net(x)
- print(out[0].size())
- print(out[1].size())
- print(out[2].size())
- net.get_params()
diff --git a/spaces/sriramelango/Social_Classification_Public/data/file_dataset.py b/spaces/sriramelango/Social_Classification_Public/data/file_dataset.py
deleted file mode 100644
index 0dcbe9a3e02cb5503d02e2511c75a2871fcc70f6..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/data/file_dataset.py
+++ /dev/null
@@ -1,102 +0,0 @@
-import os
-import torch
-import pickle
-
-
-class FileDataset:
- def __init__(self, file_path, selected_col_ids=None, dtypes=None, separator="\t", cached_index=False):
- self.file_path = file_path
- assert os.path.exists(self.file_path), "Error: The local datafile {} not exists!".format(self.file_path)
-
- self.separator = separator
- if selected_col_ids is None:
- # default to all fields
- self.selected_col_ids = list(
- range(len(open(self.file_path).readline().rstrip("\n").split(self.separator))))
- else:
- self.selected_col_ids = [int(col_id) for col_id in selected_col_ids.split(",")]
- if dtypes is None:
- # default to str
- self.dtypes = [str for col_id in self.selected_col_ids]
- else:
- self.dtypes = [eval(col_dtype) for col_dtype in dtypes.split(",")]
- assert len(self.dtypes) == len(self.selected_col_ids)
-
- self.data_cnt = 0
- try:
- self.slice_id = torch.distributed.get_rank()
- self.slice_count = torch.distributed.get_world_size()
- except Exception:
- self.slice_id = 0
- self.slice_count = 1
- self.cached_index = cached_index
- self._init_seek_index()
- self._reader = self._get_reader()
- print("file {} slice_id {} row count {} total row count {}".format(
- self.file_path, self.slice_id, self.row_count, self.total_row_count)
- )
-
- def _init_seek_index(self):
- if self.cached_index:
- cache_path = "{}.index".format(self.file_path)
- assert os.path.exists(cache_path), "cache file {} not exists!".format(cache_path)
- self.total_row_count, self.lineid_to_offset = pickle.load(open(cache_path, "rb"))
- print("local datafile {} slice_id {} use cached row_count and line_idx-to-offset mapping".format(
- self.file_path, self.slice_id))
- else:
- # make an iteration over the file to get row_count and line_idx-to-offset mapping
- fp = open(self.file_path, "r")
- print("local datafile {} slice_id {} begin to initialize row_count and line_idx-to-offset mapping".format(
- self.file_path, self.slice_id))
- self.total_row_count = 0
- offset = 0
- self.lineid_to_offset = []
- for line in fp:
- self.lineid_to_offset.append(offset)
- self.total_row_count += 1
- offset += len(line)
- self._compute_start_pos_and_row_count()
- print("local datafile {} slice_id {} finished initializing row_count and line_idx-to-offset mapping".format(
- self.file_path, self.slice_id))
-
- def _compute_start_pos_and_row_count(self):
- self.row_count = self.total_row_count // self.slice_count
- if self.slice_id < self.total_row_count - self.row_count * self.slice_count:
- self.row_count += 1
- self.start_pos = self.row_count * self.slice_id
- else:
- self.start_pos = self.row_count * self.slice_id + (self.total_row_count - self.row_count * self.slice_count)
-
- def _get_reader(self):
- fp = open(self.file_path, "r")
- fp.seek(self.lineid_to_offset[self.start_pos])
- return fp
-
- def _seek(self, offset=0):
- try:
- print("slice_id {} seek offset {}".format(self.slice_id, self.start_pos + offset))
- self._reader.seek(self.lineid_to_offset[self.start_pos + offset])
- self.data_cnt = offset
- except Exception:
- print("slice_id {} seek offset {}".format(self.slice_id, offset))
- self._reader.seek(self.lineid_to_offset[offset])
- self.data_cnt = offset
-
- def __del__(self):
- self._reader.close()
-
- def __len__(self):
- return self.row_count
-
- def get_total_row_count(self):
- return self.total_row_count
-
- def __getitem__(self, index):
- if self.data_cnt == self.row_count:
- print("reach the end of datafile, start a new reader")
- self.data_cnt = 0
- self._reader = self._get_reader()
- column_l = self._reader.readline().rstrip("\n").split(self.separator)
- self.data_cnt += 1
- column_l = [dtype(column_l[col_id]) for col_id, dtype in zip(self.selected_col_ids, self.dtypes)]
- return column_l
\ No newline at end of file
diff --git a/spaces/stable-bias/diffusion-bias-explorer/app.py b/spaces/stable-bias/diffusion-bias-explorer/app.py
deleted file mode 100644
index 9bbdc3720adecfbac17940a4da340fd9117672c6..0000000000000000000000000000000000000000
--- a/spaces/stable-bias/diffusion-bias-explorer/app.py
+++ /dev/null
@@ -1,102 +0,0 @@
-import gradio as gr
-import random, os, shutil
-from PIL import Image
-import pandas as pd
-import tempfile
-
-def open_sd_ims(adj, group, seed):
- if group != '':
- if adj != '':
- prompt=adj+'_'+group.replace(' ','_')
- if os.path.isdir(prompt) == False:
- shutil.unpack_archive('zipped_images/stablediffusion/'+ prompt.replace(' ', '_') +'.zip', prompt, 'zip')
- else:
- prompt=group
- if os.path.isdir(prompt) == False:
- shutil.unpack_archive('zipped_images/stablediffusion/'+ prompt.replace(' ', '_') +'.zip', prompt, 'zip')
- imnames= os.listdir(prompt+'/Seed_'+ str(seed)+'/')
- images = [(Image.open(prompt+'/Seed_'+ str(seed)+'/'+name)) for name in imnames]
- return images[:9]
-
-def open_ims(model, adj, group):
- seed = 48040
- with tempfile.TemporaryDirectory() as tmpdirname:
- print('created temporary directory', tmpdirname)
- if model == "Dall-E 2":
- if group != '':
- if adj != '':
- prompt=adj+'_'+group.replace(' ','_')
- if os.path.isdir(tmpdirname + '/' + model.replace(' ','').lower()+ '/'+ prompt) == False:
- shutil.unpack_archive('zipped_images/'+ model.replace(' ','').lower()+ '/'+ prompt.replace(' ', '_') +'.zip', tmpdirname+ '/'+ model.replace(' ','').lower()+ '/'+ prompt, 'zip')
- else:
- prompt=group
- if os.path.isdir(tmpdirname + '/' + model.replace(' ','').lower()+ '/'+ prompt) == False:
- shutil.unpack_archive('zipped_images/' + model.replace(' ','').lower() + '/'+ prompt.replace(' ', '_') +'.zip', tmpdirname + '/' + model.replace(' ','').lower()+ '/' + prompt, 'zip')
- imnames= os.listdir(tmpdirname + '/' + model.replace(' ','').lower()+ '/'+ prompt+'/')
- images = [(Image.open(tmpdirname + '/' + model.replace(' ','').lower()+ '/'+ prompt+'/'+name)).convert("RGB") for name in imnames]
- return images[:9]
-
- else:
- if group != '':
- if adj != '':
- prompt=adj+'_'+group.replace(' ','_')
- if os.path.isdir(tmpdirname + '/' + model.replace(' ','').lower()+ '/'+ prompt) == False:
- shutil.unpack_archive('zipped_images/'+ model.replace(' ','').lower()+ '/'+ prompt.replace(' ', '_') +'.zip', tmpdirname + '/' +model.replace(' ','').lower()+ '/'+ prompt, 'zip')
- else:
- prompt=group
- if os.path.isdir(tmpdirname + '/' + model.replace(' ','').lower()+ '/'+ prompt) == False:
- shutil.unpack_archive('zipped_images/' + model.replace(' ','').lower() + '/'+ prompt.replace(' ', '_') +'.zip', tmpdirname + '/' + model.replace(' ','').lower()+'/'+ prompt, 'zip')
- imnames= os.listdir(tmpdirname + '/' + model.replace(' ','').lower()+ '/'+ prompt+'/'+'Seed_'+ str(seed)+'/')
- images = [(Image.open(tmpdirname + '/' + model.replace(' ','').lower()+ '/'+ prompt +'/'+'Seed_'+ str(seed)+'/'+name)) for name in imnames]
- return images[:9]
-
-
-vowels = ["a","e","i","o","u"]
-prompts = pd.read_csv('promptsadjectives.csv')
-
-seeds = [46267, 48040, 51237, 54325, 60884, 64830, 67031, 72935, 92118, 93109]
-
-m_adjectives = prompts['Masc-adj'].tolist()[:10]
-f_adjectives = prompts['Fem-adj'].tolist()[:10]
-adjectives = sorted(m_adjectives+f_adjectives)
-#adjectives = ['attractive','strong']
-adjectives.insert(0, '')
-professions = sorted([p.lower() for p in prompts['Occupation-Noun'].tolist()])
-models = ["Stable Diffusion 1.4", "Dall-E 2","Stable Diffusion 2"]
-
-with gr.Blocks() as demo:
- gr.Markdown("# Diffusion Bias Explorer")
- gr.Markdown("## Choose from the prompts below to explore how the text-to-image models like [Stable Diffusion v1.4](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original), [Stable Diffion v.2](https://huggingface.co/stabilityai/stable-diffusion-2) and [DALLE-2](https://openai.com/dall-e-2/) represent different professions and adjectives")
- gr.HTML("""⚠️ DISCLAIMER: the images displayed by this tool were generated by text-to-image models and may depict offensive stereotypes or contain explicit content.""")
-# gr.Markdown("Some of the images for Dall-E 2 are missing -- we are still in the process of generating them! If you get an 'error', please pick another prompt.")
-# seed_choice = gr.State(0)
-# seed_choice = 93109
-# print("Seed choice is: " + str(seed_choice))
- with gr.Row():
- with gr.Column():
- model1 = gr.Dropdown(models, label = "Choose a model to compare results", value = models[0], interactive=True)
- adj1 = gr.Dropdown(adjectives, label = "Choose a first adjective (or leave this blank!)", interactive=True)
- choice1 = gr.Dropdown(professions, label = "Choose a first group", interactive=True)
-# seed1= gr.Dropdown(seeds, label = "Choose a random seed to compare results", value = seeds[1], interactive=True)
- images1 = gr.Gallery(label="Images").style(grid=[3], height="auto")
- with gr.Column():
- model2 = gr.Dropdown(models, label = "Choose a model to compare results", value = models[0], interactive=True)
- adj2 = gr.Dropdown(adjectives, label = "Choose a second adjective (or leave this blank!)", interactive=True)
- choice2 = gr.Dropdown(professions, label = "Choose a second group", interactive=True)
-# seed2= gr.Dropdown(seeds, label = "Choose a random seed to compare results", value= seeds[1], interactive=True)
- images2 = gr.Gallery(label="Images").style(grid=[3], height="auto")
-
- gr.Markdown("### [Research](http://gender-decoder.katmatfield.com/static/documents/Gaucher-Friesen-Kay-JPSP-Gendered-Wording-in-Job-ads.pdf) has shown that \
- certain words are considered more masculine- or feminine-coded based on how appealing job descriptions containing these words \
- seemed to male and female research participants and to what extent the participants felt that they 'belonged' in that occupation.")
-
-
- #demo.load(random_image, None, [images])
- choice1.change(open_ims, [model1, adj1,choice1], [images1])
- choice2.change(open_ims, [model2, adj2,choice2], [images2])
- adj1.change(open_ims, [model1, adj1, choice1], [images1])
- adj2.change(open_ims, [model2, adj2, choice2], [images2])
-# seed1.change(open_ims, [adj1,choice1,seed1], [images1])
-# seed2.change(open_ims, [adj2,choice2,seed2], [images2])
-
-demo.launch()
diff --git a/spaces/stomexserde/gpt4-ui/Examples/AnyTrans 7.0.4 Crack License Code Torrent 2019 [Mac Win].md b/spaces/stomexserde/gpt4-ui/Examples/AnyTrans 7.0.4 Crack License Code Torrent 2019 [Mac Win].md
deleted file mode 100644
index 866d1b7c92fa8ab8b6d98de684ce4d1e659014cc..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/AnyTrans 7.0.4 Crack License Code Torrent 2019 [Mac Win].md
+++ /dev/null
@@ -1,30 +0,0 @@
-
-
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Download online videos from over 1000 sites and convert them to compatible formats for your iOS devices.
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Access and manage your iCloud data with more ease and privacy. You can view, edit, download, or delete your iCloud files as you want.
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-
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-
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-
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-
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-
-
AnyTrans has received many positive reviews from users and experts alike. Here are some of the testimonials from satisfied customers:
-
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-
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cec2833e83
-
-
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/HD Online Player (Ra.One Full Tamil Movie Hd 1080p) Extra Quality.md b/spaces/stomexserde/gpt4-ui/Examples/HD Online Player (Ra.One Full Tamil Movie Hd 1080p) Extra Quality.md
deleted file mode 100644
index 724f17cc29f372d5bd9cb57a6b91c1ad6736be5e..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/HD Online Player (Ra.One Full Tamil Movie Hd 1080p) Extra Quality.md
+++ /dev/null
@@ -1,25 +0,0 @@
-
-
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-
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- 7196e7f11a
-
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\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Indusoft 61 Crack _HOT_.md b/spaces/stomexserde/gpt4-ui/Examples/Indusoft 61 Crack _HOT_.md
deleted file mode 100644
index 69a0f3d1c22e543690d33d2cb0828455e4219f98..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Indusoft 61 Crack _HOT_.md
+++ /dev/null
@@ -1,48 +0,0 @@
-
-
Indusoft 61 Crack: What You Need to Know
` | Indusoft 61 Crack: What You Need to Know | | `
Indusoft Web Studio is a powerful, integrated collection of automation tools that enables you to build full-featured SCADA (Supervisory Control and Data Acquisition) or HMI (Human-Machine Interface) programs for your industrial automation business. It supports various operating systems, devices, protocols, and databases, and offers a user-friendly development environment, rich graphics, web publishing, data logging, alarm management, and more.
` | Indusoft Web Studio is a powerful, integrated collection of automation tools that enables you to build full-featured SCADA (Supervisory Control and Data Acquisition) or HMI (Human-Machine Interface) programs for your industrial automation business. It supports various operating systems, devices, protocols, and databases, and offers a user-friendly development environment, rich graphics, web publishing, data logging, alarm management, and more. | | `
Indusoft Web Studio is widely used in various industries and applications, such as petrochemicals, water treatment, pipelines, power generation, manufacturing, engineering, construction, and more. It is also compatible with other Siemens products and solutions, such as SIMATIC SCADA systems. However, Indusoft Web Studio is not a free software. You need to purchase a license to use it legally and fully.
` | Indusoft Web Studio is widely used in various industries and applications, such as petrochemicals, water treatment, pipelines, power generation, manufacturing, engineering, construction, and more. It is also compatible with other Siemens products and solutions, such as SIMATIC SCADA systems. However, Indusoft Web Studio is not a free software. You need to purchase a license to use it legally and fully. | | `
Some users may not want to pay for a license or may not have access to one for various reasons. They may look for a way to bypass the license activation and use the software for free. This is where a crack comes in. A crack is a modified version of a software or a tool that allows you to use it without paying for it or following its terms of use. In this article, we will explain what a crack is, how to crack Indusoft 61, and what are the alternatives to cracking it. We will also discuss the risks and drawbacks of using a cracked software, and why you should avoid it if possible.
` | is, how to crack Indusoft 61, and what are the alternatives to cracking it. We will also discuss the risks and drawbacks of using a cracked software, and why you should avoid it if possible. | | `
What is a Crack?
` | What is a Crack? | | `
A crack is a modification of a software or a tool that allows you to use it without paying for it or following its terms of use. A crack can be a file that replaces the original executable file of the software, a keygen that generates a valid serial number or activation code for the software, or a patch that modifies the code of the software to remove the license verification.
` | A crack is a modification of a software or a tool that allows you to use it without paying for it or following its terms of use. A crack can be a file that replaces the original executable file of the software, a keygen that generates a valid serial number or activation code for the software, or a patch that modifies the code of the software to remove the license verification. | | `
Cracking software is illegal and unethical, as it violates the intellectual property rights of the software developers and distributors. It also deprives them of their revenue and incentive to create and improve their products. Moreover, cracking software can have serious consequences for the users, such as:
` | Cracking software is illegal and unethical, as it violates the intellectual property rights of the software developers and distributors. It also deprives them of their revenue and incentive to create and improve their products. Moreover, cracking software can have serious consequences for the users, such as: | | `
Malware infection: Many cracks are infected with viruses, trojans, worms, ransomware, spyware, adware, or other malicious programs that can harm your computer, steal your data, compromise your security, or extort you for money. Some cracks may also contain backdoors that allow hackers to remotely access your system and control it.
` | - Malware infection: Many cracks are infected with viruses, trojans, worms, ransomware, spyware, adware, or other malicious programs that can harm your computer, steal your data, compromise your security, or extort you for money. Some cracks may also contain backdoors that allow hackers to remotely access your system and control it. | | `
Performance issues: Cracks may cause your software to run slower, crash frequently, display errors, or malfunction in some ways. Cracks may also interfere with other programs or processes on your computer and affect their performance.
` | - Performance issues: Cracks may cause your software to run slower, crash frequently, display errors, or malfunction in some ways. Cracks may also interfere with other programs or processes on your computer and affect their performance. | | `
Lack of updates and support: Cracks may prevent you from receiving updates and patches from the software developers that fix bugs, improve features, enhance security, and add compatibility with new technologies. Cracks may also make you ineligible for technical support or customer service from the software providers.
` | - Lack of updates and support: Cracks may prevent you from receiving updates and patches from the software developers that fix bugs, improve features, enhance security, and add compatibility with new technologies. Cracks may also make you ineligible for technical support or customer service from the software providers. | | `
Legal risks: Cracking software is a form of piracy that can expose you to legal actions from the software owners or authorities. You may face fines, lawsuits, criminal charges, or even jail time for violating the copyright laws and agreements .
` | - Legal risks: Cracking software is a form of piracy that can expose you to legal actions from the software owners or authorities. You may face fines, lawsuits, criminal charges, or even jail time for violating the copyright laws and agreements.
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As you can see, cracking software is not worth the trouble and the risk. You may end up paying more than the price of the software in terms of damages, losses, or penalties. You may also compromise the quality and reliability of your software and your system. Therefore, we strongly advise you to avoid cracking software and use legal and safe methods instead.
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How to Crack Indusoft 61?
-
Despite the warnings and the consequences, some users may still want to crack Indusoft 61 and use it for free. If you are one of them, you should know that cracking Indusoft 61 is not easy or straightforward. You need to have some technical skills and knowledge, as well as some tools and resources. Here are the steps to crack Indusoft 61 using a keygen or a patch file:
-
Download Indusoft Web Studio 6.1 from the official website or from another trusted source. You can choose between the full version or the runtime version, depending on your needs.
-
Install Indusoft Web Studio 6.1 on your computer. Follow the instructions on the screen and accept the terms and conditions. Do not run the software yet.
-
Download a keygen or a patch file for Indusoft 61 from a reliable source. You can search for them on various websites, forums, blogs, or torrent sites that offer cracks for software. However, be careful of fake or malicious files that may harm your computer or steal your information. Scan the files with an antivirus program before opening them.
-
Run the keygen or the patch file as an administrator. Depending on the type of file, you may need to enter some information, such as your name, email, or serial number. The file will generate a valid activation code or modify the original executable file of Indusoft 61 to remove the license verification.
-
Copy and paste the activation code into Indusoft Web Studio 6.1 or replace the original executable file with the modified one. You may need to restart your computer or disable your internet connection for the crack to work.
-
Enjoy using Indusoft 61 for free. However, remember that you are using a cracked software that may have some problems, such as errors, bugs, crashes, malware, or legal issues.
-
These are the general steps to crack Indusoft 61 using a keygen or a patch file. However, they may vary depending on the source and the type of file you use. You may also encounter some difficulties or errors during the process that may require some troubleshooting or additional steps. Therefore, we do not recommend cracking Indusoft 61 unless you know what you are doing and are willing to take the risks.
-
Alternatives to Cracking Indusoft 61
-
If you want to use Indusoft 61 without cracking it, there are some legitimate ways to do so. Here are some alternatives to cracking Indusoft 61 that you can consider:
-
-
Download a free trial version: Indusoft Web Studio offers a free trial version that you can download from their website. The trial version allows you to use all the features and functions of Indusoft 61 for a limited time (usually 30 days). This is a great way to test and evaluate the software before buying it.
-
Purchase a license: The best way to use Indusoft 61 legally and fully is to purchase a license from Siemens or from an authorized reseller. A license gives you access to updates, patches, support, and other benefits from the software provider. The price of a license depends on the type and number of licenses you need (such as development license, runtime license, web thin client license, etc.). You can contact Siemens or a reseller for more information and pricing details.
-
Use an older version: If you have an older version of Indusoft Web Studio that is compatible with your system and meets your needs, you can use it instead of upgrading to Indusoft 61. However, you may miss out on some new features, improvements, security enhancements, and compatibility with new technologies that Indusoft 61 offers.
-
These are some alternatives to cracking Indusoft 61 that you can try. They are legal and safe ways to use the software without breaking any laws or agreements. They also ensure that you get the best performance and quality from your software and your system.
-
Conclusion
-
In this article, we have explained what Indusoft Web Studio is, what it does, and why it is popular among industrial automation users. We have also discussed what a crack is, how to crack Indusoft 61, and what are the alternatives to cracking it. We have highlighted the risks and drawbacks of using a cracked software, and why you should avoid it if possible. We hope that this article has been helpful and informative for you. Here are some FAQs that may answer some of your questions about Indusoft 61 and cracking software in general.
-
FAQs
-
What is the difference between Indusoft Web Studio and SIMATIC WinCC?
-
Indusoft Web Studio and SIMATIC WinCC are both SCADA/HMI software products from Siemens that enable you to create, monitor, and control industrial automation applications. However, they have some differences in terms of features, functions, compatibility, and pricing. For example, Indusoft Web Studio supports more operating systems, devices, protocols, and databases than SIMATIC WinCC. Indusoft Web Studio also offers web publishing, data logging, alarm management, and other features that SIMATIC WinCC does not. On the other hand, SIMATIC WinCC has more integration options with other Siemens products and solutions, such as SIMATIC S7 PLCs, SIMATIC PCS 7 process control systems, and SIMATIC SCADA systems. SIMATIC WinCC also has more advanced graphics, scripting, and reporting capabilities than Indusoft Web Studio. The price of each software depends on the type and number of licenses you need. You can compare the features and functions of both software on their respective websites or contact Siemens or a reseller for more information and pricing details.
-
How can I update my Indusoft Web Studio to the latest version?
-
If you have a valid license for Indusoft Web Studio, you can update your software to the latest version by downloading the update file from the official website or from another trusted source. You can also check for updates within the software by clicking on Help > Check for Updates. The update file will install the latest patches and fixes for your software and enhance its performance and security. However, if you are using a cracked version of Indusoft Web Studio, you may not be able to update your software or receive any updates from the software provider. You may also encounter some errors or compatibility issues with your software or your system after updating it.
-
How can I get technical support or customer service for Indusoft Web Studio?
-
If you have a valid license for Indusoft Web Studio, you can get technical support or customer service from Siemens or from an authorized reseller. You can contact them by phone, email, chat, or online form. You can also access their online resources, such as manuals, tutorials, videos, forums, blogs, webinars, and FAQs on their website or on other platforms. They will assist you with any issues or questions you may have about your software or your system. However, if you are using a cracked version of Indusoft Web Studio, you may not be eligible for technical support or customer service from the software provider or the reseller. You may also have difficulty finding reliable online resources or solutions for your problems.
-
How can I protect my computer from malware infection when downloading cracks?
-
If you decide to download cracks for software from the internet, you should be very careful of malware infection that may harm your computer or steal your information. Here are some tips to protect your computer from malware infection when downloading cracks:
-
Use a reputable antivirus program and keep it updated. Scan the files before opening them and delete any suspicious or infected files.
-
Use a firewall and a VPN to block unauthorized access to your system and encrypt your data.
-
Use a browser with security features and extensions that warn you of malicious websites or downloads.
-
Download cracks only from trusted sources that have positive reviews and feedback from other users.
-
Avoid clicking on pop-ups, ads, links, or attachments that may redirect you to malicious websites or downloads.
-
Backup your data regularly in case of data loss or corruption.
-
However, these tips are not foolproof and may not prevent all malware infections. The best way to avoid malware infection is to avoid downloading cracks altogether and use legal and safe methods instead.
-
What are some other SCADA/HMI software products that I can use instead of Indusoft Web Studio?
-
If you are looking for other SCADA/HMI software products that you can use instead of Indusoft Web Studio, some of the SCADA/HMI software products that you can use instead of Indusoft Web Studio, here are some examples:
-
GE CIMPLICITY HMI/SCADA: This is another SCADA/HMI software product from Siemens that offers high performance visualization and control for industrial automation applications. It has advanced graphics, scripting, reporting, and integration capabilities with other Siemens products and solutions. It also supports web publishing, data logging, alarm management, and more.
-
AVEVA Edge: This is a flexible HMI/SCADA software product from AVEVA that can run on various devices, from embedded systems to PCs to mobile devices. It has a user-friendly development environment, rich graphics, web publishing, data logging, alarm management, and more.
-
Wonderware InTouch: This is a popular HMI/SCADA software product from AVEVA that enables you to create, monitor, and control industrial automation applications. It has a powerful graphics engine, web publishing, data logging, alarm management, and more. It also integrates with other AVEVA products and solutions, such as System Platform and Historian.
-
These are some of the SCADA/HMI software products that you can use instead of Indusoft Web Studio. However, there are many more options available in the market that you can explore and compare. You should choose the software product that best suits your needs, budget, and preferences.
-
-
This is the end of the article. I hope you have enjoyed reading it and learned something new. If you have any feedback or questions, please let me know. Thank you for your attention.
b2dd77e56b
-
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diff --git a/spaces/sub314xxl/MetaGPT/metagpt/tools/search_engine_serpapi.py b/spaces/sub314xxl/MetaGPT/metagpt/tools/search_engine_serpapi.py
deleted file mode 100644
index 750184198c17873ca20c84ac3a40b0365b7f1f29..0000000000000000000000000000000000000000
--- a/spaces/sub314xxl/MetaGPT/metagpt/tools/search_engine_serpapi.py
+++ /dev/null
@@ -1,115 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time : 2023/5/23 18:27
-@Author : alexanderwu
-@File : search_engine_serpapi.py
-"""
-from typing import Any, Dict, Optional, Tuple
-
-import aiohttp
-from pydantic import BaseModel, Field, validator
-
-from metagpt.config import CONFIG
-
-
-class SerpAPIWrapper(BaseModel):
- search_engine: Any #: :meta private:
- params: dict = Field(
- default={
- "engine": "google",
- "google_domain": "google.com",
- "gl": "us",
- "hl": "en",
- }
- )
- serpapi_api_key: Optional[str] = None
- aiosession: Optional[aiohttp.ClientSession] = None
-
- class Config:
- arbitrary_types_allowed = True
-
- @validator("serpapi_api_key", always=True)
- @classmethod
- def check_serpapi_api_key(cls, val: str):
- val = val or CONFIG.serpapi_api_key
- if not val:
- raise ValueError(
- "To use, make sure you provide the serpapi_api_key when constructing an object. Alternatively, "
- "ensure that the environment variable SERPAPI_API_KEY is set with your API key. You can obtain "
- "an API key from https://serpapi.com/."
- )
- return val
-
- async def run(self, query, max_results: int = 8, as_string: bool = True, **kwargs: Any) -> str:
- """Run query through SerpAPI and parse result async."""
- return self._process_response(await self.results(query, max_results), as_string=as_string)
-
- async def results(self, query: str, max_results: int) -> dict:
- """Use aiohttp to run query through SerpAPI and return the results async."""
-
- def construct_url_and_params() -> Tuple[str, Dict[str, str]]:
- params = self.get_params(query)
- params["source"] = "python"
- params["num"] = max_results
- params["output"] = "json"
- url = "https://serpapi.com/search"
- return url, params
-
- url, params = construct_url_and_params()
- if not self.aiosession:
- async with aiohttp.ClientSession() as session:
- async with session.get(url, params=params) as response:
- res = await response.json()
- else:
- async with self.aiosession.get(url, params=params) as response:
- res = await response.json()
-
- return res
-
- def get_params(self, query: str) -> Dict[str, str]:
- """Get parameters for SerpAPI."""
- _params = {
- "api_key": self.serpapi_api_key,
- "q": query,
- }
- params = {**self.params, **_params}
- return params
-
- @staticmethod
- def _process_response(res: dict, as_string: bool) -> str:
- """Process response from SerpAPI."""
- # logger.debug(res)
- focus = ["title", "snippet", "link"]
- get_focused = lambda x: {i: j for i, j in x.items() if i in focus}
-
- if "error" in res.keys():
- raise ValueError(f"Got error from SerpAPI: {res['error']}")
- if "answer_box" in res.keys() and "answer" in res["answer_box"].keys():
- toret = res["answer_box"]["answer"]
- elif "answer_box" in res.keys() and "snippet" in res["answer_box"].keys():
- toret = res["answer_box"]["snippet"]
- elif "answer_box" in res.keys() and "snippet_highlighted_words" in res["answer_box"].keys():
- toret = res["answer_box"]["snippet_highlighted_words"][0]
- elif "sports_results" in res.keys() and "game_spotlight" in res["sports_results"].keys():
- toret = res["sports_results"]["game_spotlight"]
- elif "knowledge_graph" in res.keys() and "description" in res["knowledge_graph"].keys():
- toret = res["knowledge_graph"]["description"]
- elif "snippet" in res["organic_results"][0].keys():
- toret = res["organic_results"][0]["snippet"]
- else:
- toret = "No good search result found"
-
- toret_l = []
- if "answer_box" in res.keys() and "snippet" in res["answer_box"].keys():
- toret_l += [get_focused(res["answer_box"])]
- if res.get("organic_results"):
- toret_l += [get_focused(i) for i in res.get("organic_results")]
-
- return str(toret) + "\n" + str(toret_l) if as_string else toret_l
-
-
-if __name__ == "__main__":
- import fire
-
- fire.Fire(SerpAPIWrapper().run)
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Bihartetresult2011pdfdownload __FULL__.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Bihartetresult2011pdfdownload __FULL__.md
deleted file mode 100644
index b41975aa3fa2b7f65e4ce2c9039c86bb77c4a4a8..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Bihartetresult2011pdfdownload __FULL__.md
+++ /dev/null
@@ -1,9 +0,0 @@
-
-
https://coub.com/stories/2944322-__top__-bihartetresult2011pdfdownload https://trello.com/c/pkxlJssq/4-player-game-download-player-windows-windows-7 https://coub.com/stories/2944322-__top__-bihartetresult2011pdfdownload https://coub.com/stories/2944322-__top__-bihartetresult2011pdfdownload. New: Download and get the latest ACDSee 8 Codec Extras from Microsoft.
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https://coub.com/stories/2944322-__top__-bihartetresult2011pdfdownload. PDF : Prime Minister Narendra Modi said that the Union government is mulling options for a digital campaign which is capable of reaching out to every citizen and his/her family on how to lead and live in the 21st century.
https://coub.com/stories/2944322-__top__-bihartetresult2011pdfdownload https://coub.com/stories/2944322-__top__-bihartetresult2011pdfdownload. Dicey says: 23. januari 2022 02:41. Dicey alesi667506bad https://coub.com/stories/2944322-__top__-bihartetresult2011pdfdownload. mail microsoft products microsoft. zip files for software 2018-2019. zip files for dvdrip pdf crack for windows 10 microsoft silverlight. zip files for software 2018-2019.
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https://coub.com/stories/2944322-__top__-bihartetresult2011pdfdownload https://coub.com/stories/2944322-__top__-bihartetresult2011pdfdownload. pdf : This release contains the new VA DLLs that are part of DirectX 12.
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899543212b
-
-
\ No newline at end of file
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Cadimage Tools Archi Cad 19 Crack REPACK.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Cadimage Tools Archi Cad 19 Crack REPACK.md
deleted file mode 100644
index 063a7e64e85f745ee8cd23eafd93e78774905fc7..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Cadimage Tools Archi Cad 19 Crack REPACK.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
-
-6 MB: Freeware : ArchiCAD gives users the ability to create great architecture and increase productivity. com ... Oct 27, 2015 · GraphiSoft ArchiCAD 19 Crack And Key: GraphiSoft ArchiCAD 19 Crack is very ... Download Cadimage Installer 2. 4d29de3e1b
-
-
-
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Os Filhos Da Droga Livro Pdf 87.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Os Filhos Da Droga Livro Pdf 87.md
deleted file mode 100644
index 4c1b713870e5e0bf76642cd32a350838c4bf019f..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Os Filhos Da Droga Livro Pdf 87.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
-
- ;. ; = )
-
-Console: PSVita
-
-Genre: Action
-
-Release: Not released
-
-Publisher: Club Ecoin, LLC
-
-Developer: Ecoin (formerly Metal Gear Solid: Rising)
-
-The game's plot concerns an international terrorist group called "The Ninth Legion" who plan to carry out a terrorist attack, but when their plan is put into action, their target is discovered to be a not-so-evil research lab, called "Medusa" (which is a reference to the MGS2 game's protagonist, Solid Snake). The group then begins to unravel the truth behind the fake terrorist group, and thus their own existence.
-
-Gameplay
-
-Gameplay is similar to previous entries in the Metal Gear series, with improved animations, audio, and many changes to the controls and gameplay mechanics, but still maintaining the stealthy gameplay.
-
-Reception
-
-The game received "generally favorable reviews" on both platforms according to the review aggregation website Metacritic. In Japan, Famitsu gave it a score of all four eights for the PlayStation Vita version.
-
-References
-
-External links
-
-
-
-Category:2014 video games
-
-Category:Action video games
-
-Category:Metal Gear games
-
-Category:Metal Gear Solid
-
-Category:Open world video games
-
-Category:PlayStation Vita games
-
-Category:PlayStation Vita-only games
-
-Category:Ubisoft games
-
-Category:Video games developed in Japan
-
-Category:Video games set in Europe
-
-Category:Video games set in Japan
-
-Category:Video games set in the United States
-
-Category:Video games with downloadable contentQ:
-
-Schema.SobjectType.EnumField returns error: "No enum field Enum"
-
-I am facing the following problem :
-
-When I try to access a field of type Enum, using the following code :
-
-Schema.SobjectType.EnumField
-
-I get an error: "No enum field Enum".
-
-I am using Schema.sObjectType.getDescribe() to obtain the name of the field.
-
-Do you have any idea of why I get this error, or how I can obtain the field name?
-
-Thanks
-
-A:
-
-If your object is a sObject, the Enum field is automatically added. In your particular example, there is no Enum field, so you get the error message you posted. 4fefd39f24
-
-
-
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Gpgdragonboxusbdriverv212freedownload.md b/spaces/terfces0erbo/CollegeProjectV2/Gpgdragonboxusbdriverv212freedownload.md
deleted file mode 100644
index dd17c20e6ac1f71b083b960c5eebb136326a90d5..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Gpgdragonboxusbdriverv212freedownload.md
+++ /dev/null
@@ -1,5 +0,0 @@
-
-
# name of the file that contains the image, the start of the image is the first 128 bytes $ name of the file that contains the image, the start of the image is the first 128 bytes $ name of the file that contains the image, the start of the image is the first 128 bytes $ name of the file that contains the image, the start of the image is the first 128 bytes $ name of the file that contains the image, the start of the image is the first 128 bytes $ name of the file that contains the image, the start of the image is the first 128 bytes $ name of the file that contains the image, the start of the image is the first 128 bytes $ name of the file that contains the image, the start of the image is the first 128 bytes # the name of the file that contains the image, the start of the image is the first 128 bytes # the name of the file that contains the image, the start of the image is the first 128 bytes $ the name of the file that contains the image, the start of the image is the first 128 bytes $ the name of the file that contains the image, the start of the image is the first 128 bytes $ the name of the file that contains the image, the start of the image is the first 128 bytes $ the name of the file that contains the image, the start of the image is the first 128 bytes
but i don't know the information of the gpgdragonboxusbdriverv212freedownload file, and the documentation is not very clear, i can only guess that there is a file that its content is the information of the image, and the size of the image is 128 bytes, and the content of the file must start from the first byte that is 128 bytes, does anyone know what the file's name is?
Apna Sapna Money Money: A Hilarious Comedy of Errors
-
Apna Sapna Money Money is a 2006 Hindi comedy film directed by Sangeeth Sivan and starring Riteish Deshmukh, Shreyas Talpade, Riya Sen, Koena Mitra, Celina Jaitly, Suniel Shetty, Jackie Shroff and Anupam Kher. The film is a laugh riot that revolves around the lives of various characters who are connected by a common goal: money.
The plot follows Kishan (Riteish Deshmukh), a con artist who can disguise himself as anyone, and his friend Arjun (Shreyas Talpade), a hard-working musician who loves Shivani (Riya Sen), the daughter of a devout Hindu priest (Anupam Kher). Kishan agrees to help Arjun win over Shivani's father by posing as his aunt. However, things get complicated when Kishan falls for Julie (Koena Mitra), a dancer who is also the girlfriend of Carlos (Jackie Shroff), a notorious gangster. Meanwhile, Inspector Mane (Suniel Shetty) is on the hunt for Carlos and his loot, while Rana (Chunky Pandey), a Nepali don, is also after the same money. The film is full of hilarious situations, twists and turns as the characters cross paths and chase each other.
-
Apna Sapna Money Money is a remake of the Malayalam film Kakkakuyil (2001), which was also directed by Sangeeth Sivan. The film has some catchy songs composed by Pritam, such as "Dil Mein Baji Guitar", "Jai Jai Money" and "Paisa Paisa". The film was well received by the audience and critics alike, and was a commercial success at the box office. Apna Sapna Money Money is a perfect entertainer for those who love comedy films with a dose of romance and action.
-
-
The film boasts of a talented ensemble cast that delivers some hilarious performances. Riteish Deshmukh is the highlight of the film as he plays four different roles with ease and flair. He is especially funny as Sunaina, a woman who is actually Kishan in disguise. Shreyas Talpade is charming as Arjun, the innocent lover who gets into trouble because of Kishan. Riya Sen and Koena Mitra are glamorous as Shivani and Julie, the love interests of Arjun and Kishan respectively. Celina Jaitly is also impressive as Sania, another woman who is actually Kishan in disguise.
-
Suniel Shetty and Jackie Shroff add to the comic quotient as Inspector Mane and Carlos, the antagonists who are after the money. Anupam Kher is hilarious as Satyabol Shashtri, the priest who hates Christians and has a soft spot for his daughter. Rajpal Yadav, Chunky Pandey, Vrajesh Hirjee, Sunil Pal and Sanjay Mishra are also funny in their respective roles. The film also has some cameo appearances by Bobby Darling, Avtar Gill and Deena.
-
-
The film is directed by Sangeeth Sivan, who has also written the story. He has previously directed films like Yodha (1992), Zor (1998) and Chura Liyaa Hai Tumne (2003). He has also directed the sequel to this film, Apna Sapna Money Money 2 (2014), which features a different cast. The film is produced by Raju Farooqui and Prem Pillai under the banner of Subhash Ghai's Mukta Arts. The film has a runtime of 134 minutes and was released on 10 November 2006.
7196e7f11a
-
-
\ No newline at end of file
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/ESET Endpoint Security Antivirus 6.6.2078.5 Full With Medicine Serial Key Tips and Tricks for Optimal Performance.md b/spaces/tialenAdioni/chat-gpt-api/logs/ESET Endpoint Security Antivirus 6.6.2078.5 Full With Medicine Serial Key Tips and Tricks for Optimal Performance.md
deleted file mode 100644
index 17bf9b4953da80fe1c597f56d830072bdf54c14d..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/ESET Endpoint Security Antivirus 6.6.2078.5 Full With Medicine Serial Key Tips and Tricks for Optimal Performance.md
+++ /dev/null
@@ -1,67 +0,0 @@
-
-
ESET Endpoint Security Antivirus 6.6.2078.5 Full With Medicine Serial Key: How to Download and Install
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If you are looking for a reliable and powerful antivirus solution for your business or personal use, you might want to consider ESET Endpoint Security Antivirus 6.6.2078.5 Full With Medicine Serial Key. This is the latest version of the award-winning antivirus software from ESET, which offers comprehensive protection against all kinds of malware, ransomware, phishing, and other online threats.
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In this article, we will show you how to download and install ESET Endpoint Security Antivirus 6.6.2078.5 Full With Medicine Serial Key on your Windows PC. We will also explain some of the key features and benefits of this antivirus software, and why you should choose it over other alternatives.
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ESET Endpoint Security Antivirus 6.6.2078.5 Full With Medicine Serial Key
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Congratulations! You have successfully installed ESET Endpoint Security Antivirus 6.6.2078.5 Full With Medicine Serial Key on your PC.
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Image-Line FL.Studio 12.1.2 Signature Bundle-r4e Crack.md b/spaces/tialenAdioni/chat-gpt-api/logs/Image-Line FL.Studio 12.1.2 Signature Bundle-r4e Crack.md
deleted file mode 100644
index 9d4dab8c318d6c52d658d0f5cb80b469672a340b..0000000000000000000000000000000000000000
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How to Download and Install Image-Line FL.Studio 12.1.2 Signature Bundle-r4e Crack
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If you are looking for a powerful and versatile digital audio workstation, you might be interested in Image-Line FL.Studio 12.1.2 Signature Bundle-r4e Crack. This is a cracked version of the popular FL Studio software, which allows you to create, edit, mix and master music and audio tracks with ease. In this article, we will show you how to download and install Image-Line FL.Studio 12.1.2 Signature Bundle-r4e Crack on your Windows PC.
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Image-Line FL.Studio 12.1.2 Signature Bundle-r4e Crack is a modified version of the original FL Studio software, which is developed by Image-Line Software. FL Studio is one of the most popular and widely used digital audio workstations in the world, with over 20 years of history and millions of users. It offers a comprehensive set of features and tools for music production, such as sequencers, synthesizers, samplers, effects, plugins, MIDI controllers, recording, editing and more.
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download AMONG US Zombie Mode Mod for Free.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download AMONG US Zombie Mode Mod for Free.md
deleted file mode 100644
index 600e626e43fc3ac70b5b84fbd44f8274c10b3a8e..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download AMONG US Zombie Mode Mod for Free.md
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Among Us Zombie Animation APK: What You Need to Know
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What is Among Us Zombie Animation APK?
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If you are a fan of Among Us, the multiplayer social deduction game that took the world by storm in 2020, you might be interested in trying out a new mod that adds a twist to the gameplay: zombie animation. In this article, we will tell you everything you need to know about this mod, including what it is, how to get it, and why you should give it a try. We will also show you where to find more Among Us zombie animations online, in case you want to watch some hilarious and thrilling videos of other players' experiences.
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A brief introduction to Among Us and its popularity
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Among Us is a game that was released in 2018 by InnerSloth, an indie game studio based in Washington, USA. The game is set in a spaceship, where a group of crewmates have to work together to complete tasks and find the impostor among them, who is secretly killing them one by one. The game can be played online or locally with up to 10 players, and has four different maps to choose from.
The game became a viral sensation in 2020, thanks to the exposure from popular streamers and YouTubers, who found it entertaining and engaging to play with their friends and fans. According to Sensor Tower, a mobile app analytics platform, Among Us was downloaded more than 200 million times in 2020, making it one of the most downloaded games of the year. The game also won several awards, such as the Best Multiplayer Game and the Best Mobile Game at The Game Awards 2020.
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A description of the zombie animation mod and its features
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The zombie animation mod is a fan-made modification of Among Us that adds a new mode to the game: zombie mode. In this mode, one player is randomly chosen as the zombie at the start of the game, and their goal is to infect all the other players by touching them. The infected players become zombies as well, and they can help the original zombie hunt down the remaining crewmates. The crewmates have to avoid being infected and complete their tasks before time runs out.
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The mod also changes the appearance and behavior of the characters in the game, making them look and act like zombies. For example, the zombies have green skin, red eyes, and blood stains on their clothes. They also make groaning noises and walk with a limp. The crewmates have scared expressions on their faces and scream when they see a zombie. The mod also adds some new animations for killing and infecting players, such as biting their necks or ripping their heads off.
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The mod is not officially supported by InnerSloth
The next part of the article would be:
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Why You Should Try Among Us Zombie Animation APK
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If you are looking for a new way to spice up your Among Us gameplay, you might want to give the zombie animation mod a try. This mod adds a lot of fun and excitement to the game, as well as some challenges and surprises. Here are some of the benefits of playing with the zombie animation mod:
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The benefits of playing with the zombie animation mod
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It adds a new layer of strategy and deception to the game. As a crewmate, you have to be careful not to get infected by the zombie, and as a zombie, you have to be sneaky and cunning to catch your prey. You also have to communicate with your teammates and use your logic and intuition to figure out who is who.
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It makes the game more immersive and realistic. The mod changes the graphics and sounds of the game, making it look and feel like a horror movie. The zombies look scary and gruesome, and the crewmates look terrified and panicked. The mod also adds some gore and violence to the game, which might not be for everyone, but can add some thrill and suspense to the experience.
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It offers a different gameplay experience every time. The mod has different settings and options that you can customize, such as the number of zombies, the infection time, the kill cooldown, and the vision range. You can also choose different maps and roles to play with. This means that every game will be different and unpredictable, keeping you on your toes and entertained.
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The next part of the article would be:
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How to download and install the APK file on your device
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If you want to play Among Us with the zombie animation mod, you will need to download and install the APK file on your device. An APK file is an Android application package that contains all the files and data needed to run an app on your device. Here are the steps to follow:
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Go to a trusted website that provides the APK file for Among Us zombie animation mod, such as [APKPure](^1^) or [APKCombo](^2^). Make sure you download the latest version of the mod that is compatible with your device.
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Before you install the APK file, you will need to enable the installation of apps from unknown sources on your device. To do this, go to your device settings, then security, then toggle on the option that says "allow installation of apps from unknown sources". This will allow you to install apps that are not from the Google Play Store.
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Once you have downloaded the APK file, locate it in your device storage and tap on it to start the installation process. Follow the instructions on the screen and wait for the installation to finish.
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After the installation is done, you can launch Among Us from your app drawer and enjoy playing with the zombie animation mod. You can also join the Skeld.net server to play with other players who have the mod installed.
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Note: Installing APK files from unknown sources can pose some risks to your device and data, such as malware or viruses. Therefore, you should always be careful and only download APK files from reputable sources. You should also scan the APK file with an antivirus app before installing it. Additionally, you should backup your data and uninstall any previous versions of Among Us before installing the mod.
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The next part of the article would be:
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The challenges and fun of surviving as a crewmate or a zombie
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Playing Among Us with the zombie animation mod is not only fun, but also challenging. Depending on your role, you will have different objectives and strategies to follow. Here are some of the challenges and fun of surviving as a crewmate or a zombie:
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As a crewmate, you have to complete your tasks as fast as possible, while avoiding being infected by the zombie. You also have to cooperate with your fellow crewmates and use the emergency meetings and chat to share information and suspicions. You can also use some items, such as medkits, guns, or shields, to protect yourself or heal your teammates. However, you have to be careful not to waste them or use them on the wrong person.
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As a zombie, you have to infect all the crewmates before they finish their tasks or vote you out. You also have to work with your fellow zombies and coordinate your attacks and movements. You can use some abilities, such as venting, sabotaging, or disguising, to confuse and deceive the crewmates. However, you have to be careful not to expose yourself or get caught by the cameras or sensors.
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Whether you are a crewmate or a zombie, you will have a lot of fun playing with the zombie animation mod. You will experience some hilarious and tense moments, such as seeing a zombie pop out of a vent, or being chased by a horde of zombies, or finding out that your best friend is actually a zombie. You will also enjoy the amazing animations and sounds that make the game more immersive and realistic.
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The next part of the article would be:
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The best tips and tricks to win as either role
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Playing Among Us with the zombie animation mod can be challenging, but also rewarding. Whether you are a crewmate or a zombie, you will need some skills and strategies to win the game. Here are some of the best tips and tricks to win as either role:
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As a crewmate, you should stick together with other crewmates and avoid being alone. This way, you can protect each other from the zombie and have more witnesses to confirm your innocence. You should also use the cameras and sensors to monitor the movements of other players and spot any suspicious behavior. You should also report any dead bodies or call emergency meetings as soon as possible, to prevent the zombie from infecting more people.
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As a zombie, you should try to isolate and kill the crewmates one by one, without being seen by others. You should also use vents, sabotages, and disguises to move around the map and create chaos and confusion. You should also lie and manipulate the crewmates during meetings and chat, by accusing others, providing fake alibis, or pretending to do tasks. You should also avoid killing or infecting immune players in front of others, as they can prove your guilt.
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Playing Among Us with the zombie animation mod can be a lot of fun, but also a lot of work. You will need to use your brain and your skills to outsmart and outplay your opponents. But don't worry, with these tips and tricks, you will be able to win as either role.
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The next part of the article would be:
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Where to Find More Among Us Zombie Animations
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If you enjoyed playing or watching Among Us with the zombie animation mod, you might be wondering where to find more Among Us zombie animations online. There are many talented and creative animators who have made their own versions of Among Us zombie mode, with different styles, stories, and characters. Here are some of the best places to find more Among Us zombie animations:
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A list of YouTube channels that showcase Among Us zombie animations
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YouTube is one of the most popular platforms for watching and sharing Among Us zombie animations. There are many YouTube channels that specialize in making and uploading Among Us zombie animations, or feature them as part of their content. Here are some of the YouTube channels that showcase Among Us zombie animations:
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[Swit Dom](^1^): This is the channel that started the trend of Among Us zombie animations, with its original series that has over 100 million views. Swit Dom's animations are well-made, funny, and action-packed, with a variety of characters and scenarios. Swit Dom has also made other Among Us animations, such as Among Us vs Plants vs Zombies, Among Us vs Minecraft, and Among Us vs Fall Guys.
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[Hornstromp](^6^): This is another channel that makes high-quality and entertaining Among Us zombie animations, with its own style and humor. Hornstromp's animations are more realistic and gory than Swit Dom's, but also more creative and diverse. Hornstromp has also made other Among Us animations, such as Among Us 3D, Among Us School, and Among Us Impostor Life.
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[ACAPLANET](^3^): This is a channel that makes cute and funny Among Us zombie animations, with its own story and characters. ACAPLANET's animations are more colorful and cartoony than Swit Dom's or Hornstromp's, but also more emotional and dramatic. ACAPLANET has also made other Among Us animations, such as Among Us vs Sonic, Among Us vs Mario, and Among Us vs Pokemon.
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These are just some of the YouTube channels that showcase Among Us zombie animations. There are many more channels that you can explore and enjoy, such as OFF, ErrorMotion, MonTie, Ola, Movingwork, Cookies Among Us, and more.
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The next part of the article would be:
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A comparison of different Among Us zombie animation styles and creators
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As you can see, there are many different Among Us zombie animation styles and creators, each with their own strengths and weaknesses. Some of them are more realistic and detailed, while others are more cartoonish and simple. Some of them are more humorous and lighthearted, while others are more serious and dark. Some of them are more original and innovative, while others are more inspired and influenced by other games or media.
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There is no definitive answer to which Among Us zombie animation style or creator is the best, as it depends on your personal preference and taste. You might like some of them more than others, or you might like all of them equally. The important thing is to appreciate the hard work and creativity that goes into making these animations, and to support the animators by liking, commenting, subscribing, and sharing their videos.
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A recommendation of the best Among Us zombie animation videos to watch
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If you are looking for some of the best Among Us zombie animation videos to watch, we have some recommendations for you. These are some of the videos that we think are the most entertaining, impressive, and enjoyable to watch, based on our own opinion and the feedback from other viewers. Here are some of the best Among Us zombie animation videos to watch:
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[Among Us Zombie Animation Season 1 Full Movie] by Swit Dom: This is the video that started it all, the first season of Swit Dom's Among Us zombie animation series. It has over 100 million views and is one of the most popular Among Us videos on YouTube. It tells the story of how a group of crewmates try to survive a zombie outbreak on their spaceship, with some twists and turns along the way. It is funny, action-packed, and well-made, with great voice acting and sound effects.
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[Among Us Zombie Animation 3D] by Hornstromp: This is one of the most impressive and realistic Among Us zombie animations on YouTube. It is made in 3D, with stunning graphics and animations that make the game look like a movie. It shows how a crewmate becomes a zombie after being bitten by a rat, and how he infects his friends one by one. It is gory, violent, and intense, with some jump scares and suspense.
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[Among Us Zombie Animation - The Last Stand] by ACAPLANET: This is one of the most emotional and dramatic Among Us zombie animations on YouTube. It is made in 2D, with colorful and cute graphics and animations that contrast with the dark and tragic story. It shows how a crewmate sacrifices himself to save his friends from a horde of zombies, and how they honor his memory. It is touching, heartbreaking, and inspiring, with some humor and romance.
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These are just some of the best Among Us zombie animation videos to watch. There are many more videos that you can find and enjoy on YouTube, such as Among Us Zombie Animation - The Final Battle by Swit Dom, Among Us Zombie Animation - The Last Hope by Hornstromp, Among Us Zombie Animation - The End by ACAPLANET, and more.
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The next part of the article would be:
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Conclusion
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In conclusion, Among Us zombie animation APK is a mod that adds a new mode to the game, where one player is a zombie and tries to infect all the other players, who are crewmates. The mod also changes the graphics and sounds of the game, making it look and feel like a horror movie. The mod is fun and challenging to play, as it requires different strategies and skills for each role. The mod is also easy to download and install on your device, as long as you follow the steps and precautions we mentioned.
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If you are a fan of Among Us and zombies, you should definitely try out this mod and see for yourself how it changes the game. You will have a lot of fun and excitement playing with your friends or other players online. You will also enjoy watching more Among Us zombie animations on YouTube, as there are many talented and creative animators who have made their own versions of the mod, with different styles, stories, and characters.
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We hope you found this article helpful and informative. If you did, please share it with your friends and family who might be interested in Among Us zombie animation APK. Also, let us know what you think about the mod and the animations in the comments below. We would love to hear your feedback and opinions.
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Thank you for reading and have a great day!
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FAQs
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Q: Is Among Us zombie animation APK safe to use?
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A: Yes, as long as you download it from a trusted source and scan it with an antivirus app before installing it. However, you should always be careful when installing APK files from unknown sources, as they might contain malware or viruses that can harm your device or data.
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Q: Is Among Us zombie animation APK compatible with all devices?
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A: No, the mod might not work on some devices or Android versions. You should check the compatibility of the mod before downloading it, and make sure you have enough storage space and battery life on your device.
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Q: Can I play Among Us zombie animation APK with other mods or versions of the game?
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A: No, the mod is not compatible with other mods or versions of the game. You should uninstall any previous versions of Among Us before installing the mod, and only play with other players who have the same mod installed.
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Q: Can I play Among Us zombie animation APK offline or locally?
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A: Yes, you can play the mod offline or locally with up to 10 players. However, you will need an internet connection to download and install the mod, and to join the Skeld.net server if you want to play with other players online.
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Q: Where can I find more information or support for Among Us zombie animation APK?
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A: You can find more information or support for the mod on its official website, [Skeld.net], or on its Discord server, [Skeld.net Discord]. You can also contact the developers or the community members there if you have any questions or issues with the mod.
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Garten of Banban 2 Mobile and Explore the Horrors of the Underground Facility.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Garten of Banban 2 Mobile and Explore the Horrors of the Underground Facility.md
deleted file mode 100644
index 96f3fe623c7b48c3fb56c383dc56895dca88b0e2..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Garten of Banban 2 Mobile and Explore the Horrors of the Underground Facility.md
+++ /dev/null
@@ -1,145 +0,0 @@
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How to Download Garten of Banban 2 Mobile
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If you are a fan of horror adventure games, you might have heard of Garten of Banban 2, a game inspired by a mysterious kindergarten that hides a dark secret. In this article, we will show you how to download this game on your mobile device and enjoy its thrilling gameplay.
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The game follows the story of a curious character who visits Banban Kindergarten and discovers that it has a massive underground facility. As you explore the abandoned kindergarten, you will encounter various puzzles, enemies, and secrets that will reveal the horrifying truth behind the place.
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The game belongs to the genre of action-adventure survival horror casual games. It features beautiful graphics, sound effects, voice acting, and animations that create an immersive atmosphere. The game also has multiple endings depending on your choices and actions.
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Here are some reasons why you should play Garten of Banban 2 Mobile:
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It has an engaging story that will keep you hooked until the end.
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It has realistic sound effects that will enhance your sense of fear and suspense.
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It has multiple endings that will make you want to replay the game and see different outcomes.
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How to download Garten of Banban 2 Mobile for Android?
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If you have an Android device, you can download Garten of Banban 2 Mobile from Google Play Store. Here are the steps you need to follow:
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Open Google Play Store on your device and search for Garten of Banban 2 Mobile in the search bar.
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Tap on the game icon and then tap on the Install button.
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Wait for the game to download and install on your device. The game size is about 200 MB, so make sure you have enough space and a stable internet connection.
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Once the game is installed, you can tap on the Open button or find the game icon on your home screen or app drawer.
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You can also download the game from the official website of Euphoric Brothers Games. Here are the steps you need to follow:
Tap on the Download for Android button and then tap on OK to confirm.
-
Wait for the game file to download on your device. The file name is Garten_of_Banban_2_Mobile.apk.
-
Go to your device settings and enable Unknown sources under Security. This will allow you to install apps from sources other than Google Play Store.
-
Go to your file manager and locate the downloaded game file. Tap on it and then tap on Install.
-
Wait for the game to install on your device and then tap on Open.
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How to install Garten of Banban 2 Mobile on your Android device?
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If you have downloaded the game from Google Play Store, you don't need to do anything else to install it. The game will automatically install on your device after downloading it.
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If you have downloaded the game from the official website, you need to follow these steps to install it:
-
-
Go to your device settings and enable Unknown sources under Security. This will allow you to install apps from sources other than Google Play Store.
-
Go to your file manager and locate the downloaded game file. Tap on it and then tap on Install.
-
Wait for the game to install on your device and then tap on Open.
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How to play Garten of Banban 2 Mobile on your Android device?
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To play Garten of Banban 2 Mobile on your Android device, you need to follow these steps:
-
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Launch the game by tapping on its icon or opening it from your app drawer.
-
Select your preferred language from the options available. The game supports English, Korean, Japanese, Chinese, and Spanish.
-
Tap on the New Game button to start a new game or tap on the Load Game button to continue a previous game.
-
Tap on the screen to skip the intro or watch it if you want to know more about the story.
-
Tap on the screen again to enter the main menu. Here you can adjust the settings, view the achievements, or exit the game.
-
Tap on the Start Game button to begin playing. You can also select a difficulty level from Easy, Normal, or Hard.
-
To control your character, use the virtual joystick on the left side of the screen. To interact with objects or people, tap on them. To access your inventory, tap on the backpack icon on the right side of the screen. To pause the game, tap on the menu icon on the top right corner of the screen.
-
To solve puzzles, you need to find clues, items, or codes that will help you unlock doors, safes, or cabinets. To avoid enemies, you need to hide, run, or fight them. To find secrets, you need to explore every corner of the kindergarten and look for hidden passages, rooms, or items.
-
To get different endings, you need to make different choices and actions throughout the game. Some choices will affect your relationship with other characters, while some actions will affect your karma level. Your ending will depend on how you interact with others and how you behave in the kindergarten.
-
-
How to download Garten of Banban 2 Mobile for iOS?
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If you have an iOS device, you can download Garten of Banban 2 Mobile from App Store. Here are the steps you need to follow:
-
-
Open App Store on your device and search for Garten of Banban 2 Mobile in the search bar.
-
Tap on the game icon and then tap on the Get button.
-
Wait for the game to download and install on your device. The game size is about 200 MB, so make sure you have enough space and a stable internet connection.
-
Once the game is installed, you can tap on the Open button or find the game icon on your home screen or app library.
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How to install Garten of Banban 2 Mobile on your iOS device?
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If you have downloaded the game from App Store, you don't need to do anything else to install it. The game will automatically install on your device after downloading it.
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How to play Garten of Banban 2 Mobile on your iOS device?
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To play Garten of Banban 2 Mobile on your iOS device, you need to follow these steps:
-
-
Launch the game by tapping on its icon or opening it from your app library.
-
Select your preferred language from the options available. The game supports English, Korean, Japanese, Chinese, and Spanish.
-
Tap on the New Game button to start a new game or tap on the Load Game button to continue a previous game.
-
Tap on the screen to skip the intro or watch it if you want to know more about the story.
-
Tap on the screen again to enter the main menu. Here you can adjust the settings, view the achievements, or exit the game.
-
Tap on the Start Game button to begin playing. You can also select a difficulty level from Easy, Normal, or Hard.
-
To control your character, swipe on the left side of the screen. To interact with objects or people, tap on them. To access your inventory, tap on the backpack icon on the right side of the screen. To pause the game, tap on the menu icon on the top right corner of the screen.
-
To solve puzzles, you need to find clues, items, or codes that will help you unlock doors, safes, or cabinets. To avoid enemies, you need to hide, run, or fight them. To find secrets, you need to explore every corner of the kindergarten and look for hidden passages, rooms, or items.
-
To get different endings, you need to make different choices and actions throughout the game. Some choices will affect your relationship with other characters, while some actions will affect your karma level. Your ending will depend on how you interact with others and how you behave in the kindergarten.
-
-
Tips and tricks for playing Garten of Banban 2 Mobile
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If you want to have a better gaming experience with Garten of Banban 2 Mobile, here are some tips and tricks that might help you:
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-
Save your game frequently. The game does not have an auto-save feature, so you need to manually save your progress by tapping on the save icon in the menu. This will prevent you from losing your data if you encounter a bug or crash.
-
Use headphones. The game has amazing sound effects that will enhance your immersion and scare you more. Using headphones will also help you hear important sounds that might give you clues or warn you of danger.
-
Explore everything. The kindergarten is full of secrets and hidden items that will help you solve puzzles or unlock achievements. Don't be afraid to look around and interact with everything you see. You might find something useful or interesting.
-
Be careful with your choices. The game has multiple endings that depend on your choices and actions. Some choices will affect how other characters treat you or react to you. Some actions will affect your karma level, which will determine your fate in the end. Think carefully before making a decision and be ready to face the consequences.
-
Have fun. The game is meant to be enjoyed and not taken too seriously. Don't get frustrated if you get stuck or die. Just try again and learn from your mistakes. The game has a lot of humor and charm that will make you smile and laugh along the way.
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-
Frequently asked questions about Garten of Banban 2 Mobile
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Here are some common questions and answers about Garten of Banban 2 Mobile:
-
How much does Garten of Banban 2 Mobile cost?
-
Garten of Banban 2 Mobile is a free-to-play game that does not require any in -app purchases or subscriptions. However, the game does have ads that will appear occasionally. You can remove the ads by watching a video or by supporting the developer through donations.
-
How much space does Garten of Banban 2 Mobile take?
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Garten of Banban 2 Mobile takes about 200 MB of space on your device. However, the game might require additional data to download updates or content. Make sure you have enough storage and a stable internet connection before downloading or playing the game.
-
What is the rating of Garten of Banban 2 Mobile?
-
Garten of Banban 2 Mobile is rated 12+ on Google Play Store and App Store. The game contains mild violence, horror, and suggestive themes that might not be suitable for younger audiences. Parental guidance is advised for children under 12 years old.
-
What devices are compatible with Garten of Banban 2 Mobile?
-
Garten of Banban 2 Mobile is compatible with Android devices that have Android 5.0 or higher and iOS devices that have iOS 10.0 or higher. The game might not work properly on older or lower-end devices. The developer recommends using devices that have at least 2 GB of RAM and a quad-core processor for optimal performance.
-
How often does Garten of Banban 2 Mobile get updated?
-
Garten of Banban 2 Mobile gets updated regularly by the developer to fix bugs, improve features, and add new content. The developer also listens to the feedback and suggestions of the players and tries to implement them in future updates. You can check the latest news and updates about the game on the official website, social media pages, or in-game announcements.
-
Conclusion
-
Garten of Banban 2 Mobile is a horror adventure game that will take you on a terrifying journey through a mysterious kindergarten. You will have to solve puzzles, avoid enemies, and find secrets to uncover the truth behind the place. The game has beautiful graphics, sound effects, voice acting, and animations that will immerse you in the story. The game also has multiple endings that will depend on your choices and actions.
-
If you are looking for a thrilling and fun game to play on your mobile device, you should definitely download Garten of Banban 2 Mobile and try it out. You can download it for free from Google Play Store or App Store, or from the official website of Euphoric Brothers Games. You will not regret it!
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I hope this article has helped you learn how to download Garten of Banban 2 Mobile and enjoy its gameplay. If you have any questions or comments, feel free to leave them below. Thank you for reading!
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/101 Alimentos Que Curan.pdf.md b/spaces/tioseFevbu/cartoon-converter/scripts/101 Alimentos Que Curan.pdf.md
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101 Alimentos Que Curan: Una GuÃa Completa de los Beneficios de los Alimentos Naturales
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¿SabÃas que hay alimentos que pueden ayudarte a prevenir y combatir diversas enfermedades? ¿Quieres conocer cuáles son y cómo incorporarlos a tu dieta? Entonces, este libro es para ti.
Además, el libro te enseña cómo combinar los alimentos para potenciar sus efectos y cómo crear tu propio plan alimenticio personalizado según tus necesidades y objetivos. AsÃ, podrás disfrutar de una alimentación sana, variada y deliciosa, mientras cuidas tu cuerpo y tu mente.
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Algunos de los alimentos que encontrarás en el libro son:
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El ajo: un antibiótico natural que fortalece el sistema inmunológico y previene infecciones.
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La manzana: una fruta rica en fibra y antioxidantes que ayuda a regular el colesterol y el azúcar en sangre.
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El brócoli: un vegetal con alto contenido de vitaminas, minerales y fitoquÃmicos que protege contra el cáncer y las enfermedades cardiovasculares.
El chocolate: un alimento que mejora el ánimo, el rendimiento cognitivo y la circulación sanguÃnea, siempre que sea puro y sin azúcar añadido.
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Estos son solo algunos ejemplos de los 101 alimentos que curan que podrás descubrir en este libro. Si quieres saber más, descarga el libro en formato PDF y empieza a disfrutar de sus beneficios hoy mismo.
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¿Cómo puedes obtener el libro 101 Alimentos Que Curan.pdf? Es muy fácil. Solo tienes que hacer clic en el botón de abajo y seguir las instrucciones para descargarlo en tu dispositivo. El libro está en formato PDF, por lo que podrás leerlo en cualquier ordenador, tablet o móvil. Además, el libro tiene un precio muy asequible y una garantÃa de devolución de 60 dÃas, por si no quedas satisfecho con tu compra.
"Este libro me ha cambiado la vida. He aprendido a comer mejor y a cuidar mi salud con alimentos naturales. He notado una gran mejora en mi energÃa, mi piel, mi peso y mi estado de ánimo. Lo recomiendo a todo el mundo."
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-Juan Carlos, 45 años
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"Me encanta este libro. Tiene mucha información útil y práctica sobre los alimentos que curan. Además, las recetas son muy fáciles de hacer y muy ricas. He incorporado muchos de estos alimentos a mi dieta y me siento mucho mejor."
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Download 911.re Software For Mac Os Fixed.md b/spaces/tioseFevbu/cartoon-converter/scripts/Download 911.re Software For Mac Os Fixed.md
deleted file mode 100644
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--- a/spaces/tioseFevbu/cartoon-converter/scripts/Download 911.re Software For Mac Os Fixed.md
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How to Download 911.re Software For Mac Os and Stay Anonymous Online
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911.re is a popular proxy service that allows you to browse the web anonymously and securely. With 911.re, you can access any website or service that is blocked or restricted in your region, such as Netflix, Hulu, Facebook, Twitter, etc. You can also protect your privacy and identity from hackers, trackers, and snoopers who may try to steal your personal information or monitor your online activities.
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911.re software is available for Windows, Linux, and Mac OS. In this article, we will show you how to download 911.re software for Mac OS and set it up on your computer. Follow these simple steps to get started:
Visit the official website of 911.re and create an account. You will need to provide a valid email address and a password. You will also need to choose a subscription plan that suits your needs and budget. You can pay with PayPal, Bitcoin, or credit card.
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After you sign up, log in to your account and go to the "Download" section. You will see a list of software options for different platforms. Click on the "Mac OS" button and download the 911.re software for Mac OS.
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Once the download is complete, open the 911.re software file and drag it to your Applications folder. You may need to enter your administrator password to install it.
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Launch the 911.re software from your Applications folder. You will see a login screen where you need to enter your email and password that you used to create your account.
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After you log in, you will see the main interface of the 911.re software. You can choose from different proxy modes, such as "Fast", "Secure", or "Custom". You can also select a specific country or region for your proxy server.
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Click on the "Connect" button to start using the 911.re proxy service. You will see a green icon on the top right corner of your screen indicating that you are connected.
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Now you can open any browser or application that you want to use with the 911.re proxy service. You will be able to access any website or service that is normally blocked or restricted in your region. You will also be able to browse the web anonymously and securely without revealing your real IP address or location.
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That's it! You have successfully downloaded and installed 911.re software for Mac OS and started using it on your computer. Enjoy your online freedom and privacy with 911.re!
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Benefits of Using 911.re Proxy Service
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911.re proxy service has many benefits for its users, such as:
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Low cost: 911.re offers one of the cheapest proxy packages in the market. You can get 150 proxies for only $28, which is much lower than other proxy providers. You can also get unlimited lifetime access to the proxies you purchase, which means you don't have to pay any recurring fees.
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High quality: 911.re claims to have thousands of private IP addresses from 190 countries. These proxies are residential and mobile, which means they are more reliable and less likely to be detected or blocked by websites. You can also choose from different proxy modes, such as "Fast", "Secure", or "Custom", depending on your needs and preferences.
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Easy to use: 911.re provides a simple and user-friendly software that you can download and install on your Windows, Linux, or Mac OS computer. You can easily log in to your account, select a proxy from the list, and connect to it with one click. You can also switch proxies anytime you want without any hassle.
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Anonymity and security: 911.re helps you hide your real IP address and location from websites and online services that may track or monitor your online activities. You can also protect your privacy and identity from hackers, trackers, and snoopers who may try to steal your personal information or infect your device with malware.
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Access to blocked or restricted content: 911.re allows you to bypass internet censorship and geo-restrictions that may prevent you from accessing certain websites or services in your region. You can access any website or service that you want, such as Netflix, Hulu, Facebook, Twitter, etc., without any limitations or interference.
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These are some of the benefits of using 911.re proxy service. However, there are also some risks and drawbacks that you should be aware of before using it.
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Download Aushadh Darshan Pdf Free Free.md b/spaces/tioseFevbu/cartoon-converter/scripts/Download Aushadh Darshan Pdf Free Free.md
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How to Download Aushadh Darshan Pdf Free
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Aushadh Darshan is a book written by Swami Ramdev, a renowned yoga guru and founder of Patanjali Yogpeeth. The book contains various home remedies and ayurvedic medicines for various diseases and disorders. The book is based on the ancient wisdom of Ayurveda, which is considered to be the oldest and most holistic system of medicine in the world.
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If you are interested in learning more about the benefits of Ayurveda and how to use natural herbs and spices to cure your ailments, you can download Aushadh Darshan Pdf for free from the internet. There are several websites that offer this book in different languages, such as Hindi, English, Gujarati, Marathi, Punjabi, Oriya, Assamia, Bangla, Telugu,Malayalam, Tamil, Kannada and Nepali.
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Another website that offers Aushadh Darshan Pdf for free is Panotbook[^3^], which is a platform for sharing Hindi books and literature. You can download the book by clicking on this link: पतà¤à¤à¤²à¤¿ à¤à¤·à¤§ दरà¥à¤¶à¤¨ | Patanjali Aushadh Darshan PDF in Hindi. You will need to enter your email address and click on the download button to get the book. You can also read the book online or print it out.
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Aushadh Darshan is a valuable resource for anyone who wants to learn more about Ayurveda and how to use natural remedies for health and wellness. The book contains simple and effective solutions for common problems such as acidity, cough, cold, diabetes, obesity, hair loss, skin diseases, etc. The book also explains the principles of Ayurveda and how to balance the three doshas (vata, pitta and kapha) in the body.
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We hope you find this article helpful and enjoy reading Aushadh Darshan Pdf for free. If you have any questions or feedback, please feel free to leave a comment below.
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Ayurveda is not just a system of medicine, but a way of life that promotes harmony between the individual and the environment. Ayurveda believes that every person has a unique constitution or prakriti, which is determined by the combination of the five elements (air, fire, water, earth and ether) in the body. By understanding one's prakriti, one can choose the right diet, lifestyle and herbs that suit one's nature and needs.
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Ayurveda also emphasizes the importance of detoxification and rejuvenation of the body and mind. One of the ways to achieve this is by following a seasonal routine or ritucharya, which involves adjusting one's habits according to the changes in nature. For example, in winter, one should eat warm, nourishing and oily foods, drink hot beverages, wear warm clothes and do gentle exercises. In summer, one should eat light, cooling and fresh foods, drink plenty of water, wear light clothes and avoid excessive sun exposure.
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Ayurveda also recommends various therapies and treatments for specific conditions and diseases. Some of these include panchakarma (five cleansing procedures), shirodhara (pouring oil on the forehead), abhyanga (oil massage), swedana (sweating), nasya (nasal drops), basti (enema), etc. These therapies help to remove toxins from the body, improve circulation, nourish the tissues, calm the nerves and balance the doshas.
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Dum Laga Ke Haisha 4 Movie Free LINK Download Mp4.md b/spaces/tioseFevbu/cartoon-converter/scripts/Dum Laga Ke Haisha 4 Movie Free LINK Download Mp4.md
deleted file mode 100644
index 9644a95bd3cf7ea6f600a753013804592609ddf9..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Dum Laga Ke Haisha 4 Movie Free LINK Download Mp4.md
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Dum Laga Ke Haisha 4 Movie Free MP4: How to Watch the Latest Sequel of the Hit Romantic Comedy
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Dum Laga Ke Haisha is a 2015 Indian Hindi-language romantic comedy-drama film written and directed by Sharat Katariya, starring Ayushmann Khurrana and Bhumi Pednekar, Sanjay Mishra and Seema Pahwa in the lead roles. The film's background score is composed by Italian composer Andrea Guerra. The film was released on 27 February 2015 to good critical reception and won several awards, including the National Film Award for Best Feature Film in Hindi. The film tells the story of Prem, a school drop out who reluctantly marries Sandhya, an overweight and educated girl, and how they overcome their differences and fall in love.
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The film was followed by three sequels, Dum Laga Ke Haisha 2 (2017), Dum Laga Ke Haisha 3 (2019) and Dum Laga Ke Haisha 4 (2021), which continued the story of Prem and Sandhya as they face new challenges and adventures in their married life. The sequels also featured the same cast and crew as the original film, with some new additions and guest appearances.
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Flame Of Recca Tagalog Version TOP Full Movie Episodes 100l.md b/spaces/tioseFevbu/cartoon-converter/scripts/Flame Of Recca Tagalog Version TOP Full Movie Episodes 100l.md
deleted file mode 100644
index 3dba82dc9ac9b9c4fd944794b74f5097ca5d30e6..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Flame Of Recca Tagalog Version TOP Full Movie Episodes 100l.md
+++ /dev/null
@@ -1,20 +0,0 @@
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Flame Of Recca: A Classic Anime Series in Tagalog
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Flame Of Recca is a Japanese manga series written and illustrated by Nobuyuki Anzai. It was serialized in Shogakukan's Weekly ShÅnen Sunday from April 1995 to February 2002, with its chapters collected in 33 tankÅbon volumes. The series was adapted into a 42-episode anime television series by Pierrot, broadcast on Fuji TV from July 1997 to July 1998.
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Flame Of Recca Tagalog Version Full Movie Episodes 100l
The story follows Recca Hanabishi, a teenage ninja who can manipulate fire and is the last surviving member of a legendary ninja clan that served the Hokage, the leader of the fire nation. He meets Yanagi Sakoshita, a girl with healing powers, and vows to protect her from those who want to use her abilities for evil. Along the way, he forms a team of warriors with unique elemental powers and participates in a tournament to fight against the madman KÅran Mori, who seeks to unleash the power of the eight dragons that reside within Recca.
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Flame Of Recca was one of the popular anime series that aired in the Philippines by GMA Network in 1999. It was dubbed in Tagalog, the national language of the country, and gained a loyal fanbase among Filipino viewers. The series was praised for its action-packed scenes, engaging characters, and thrilling plot twists. Many fans consider it as one of the best anime shows of all time.
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However, due to some licensing issues, the Tagalog version of Flame Of Recca was never released on DVD or online streaming platforms. The only way to watch it was through reruns on TV or through pirated copies. This made it hard for fans to enjoy the series in its full glory.
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But now, there is good news for all Flame Of Recca fans in the Philippines and around the world. A YouTube channel named Tuh Nhu has uploaded all 42 episodes of Flame Of Recca in Tagalog on their channel. The videos are high-quality and have clear audio. They also have English subtitles for those who want to understand the dialogue better. The channel has over 28K subscribers and has received millions of views for their videos.
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This is a great opportunity for fans to relive their childhood memories and watch Flame Of Recca in Tagalog again. It is also a chance for new viewers to discover this classic anime series and enjoy its exciting story and characters. Flame Of Recca is a must-watch for anyone who loves anime, especially those who are into ninjas, fire, and dragons.
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To watch Flame Of Recca in Tagalog, visit Tuh Nhu's YouTube channel and click on their playlists. You can find all 42 episodes there, divided into two parts: Episode 1-21 and Episode 22-42. You can also leave your comments and feedback on their videos and share them with your friends.
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Don't miss this chance to watch Flame Of Recca in Tagalog online for free. It is a rare treat that you won't regret.
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Flame Of Recca has a large and diverse cast of characters, each with their own personality, background, and abilities. The main protagonist, Recca Hanabishi, is a cheerful and energetic boy who loves ninjas and fire. He has a strong sense of loyalty and justice, and will do anything to protect his friends and Yanagi. He can control eight flame dragons that grant him different powers, such as creating blades, explosions, illusions, and time manipulation.
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The other members of Team Hokage, Recca's allies in the tournament, are also skilled fighters with unique weapons and skills. They are FÅ«ko Kirisawa, a tomboyish girl who wields the Fujin, a madogu that can manipulate wind; Tokiya Mikagami, a calm and cold boy who uses the Ensui, a madogu that can create water swords; Domon Ishijima, a muscular and hot-headed boy who fights with his superhuman strength; and Kaoru Koganei, a young and playful boy who has the KÅgan Anki, a madogu that can transform into five different weapons.
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The main antagonist of the series is Kurei Mori, Recca's older half-brother who was supposed to be the heir of the Hokage clan. He is a cruel and ruthless man who can also control fire, but his flame is different from Recca's. He has the power of the phoenix, which allows him to create living flames that obey his will. He leads the Uruha, a group of warriors who serve under KÅran Mori, his adoptive father and the owner of the Mori Corporation. He has a twisted obsession with Yanagi, whom he believes is the reincarnation of his first love, Sakura.
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The Uruha is divided into several factions, each with their own leader and theme. They are the Uruha Ma (é), who use forbidden or dark madogu; the Uruha Kurenai (ç´ ), who are Kurei's personal guards; the Uruha Oto (é³), who use sound-based madogu; the Uruha Rai (é·), who use electric madogu; the Uruha Kurogane (é), who use madogu that enhance their physical abilities; and the Uruha Jyushinshuu (ç£ç¥è¡), who are the strongest and most loyal members of the Uruha.
-
The series also introduces other characters and groups that have their own roles and motives in the story. They include KagerÅ, Recca's mother who was transformed into an immortal being by a madogu; Neon and Miki, two female assassins who work for KÅran Mori; Genjuro, an old man who trains Recca and his friends; KÅran Mori's four executive officers: Meguri KyÅza, TendÅ Jigoku, Mori Kouran and Tsukino Mori; The Hokage clan's leader Åka and his wife Sakura; and The SODOM organization, a secret group that collects dangerous madogu and wants to destroy the world.
7b8c122e87
-
-
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Ghostbusters Tamil Dubbed Movie Download [VERIFIED].md b/spaces/tioseFevbu/cartoon-converter/scripts/Ghostbusters Tamil Dubbed Movie Download [VERIFIED].md
deleted file mode 100644
index 4239e6e784ae561cd5f2b6d4b283e3a79e863d15..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Ghostbusters Tamil Dubbed Movie Download [VERIFIED].md
+++ /dev/null
@@ -1,14 +0,0 @@
-
-
How to Watch Ghostbusters: Afterlife in Tamil Dubbed Online
-
Ghostbusters: Afterlife is a 2021 American supernatural comedy film that is a direct sequel to the original Ghostbusters movies from the 1980s. The film follows a family who moves to a small town and discovers their connection to the legacy of the Ghostbusters. The film features a mix of old and new characters, as well as plenty of ghosts and action.
If you are a fan of Ghostbusters and want to watch the film in Tamil dubbed version, you have a few options online. Here are some of them:
-
-
Netflix: Netflix has the exclusive streaming rights for Ghostbusters: Afterlife in India and other countries. You can watch the film in Tamil dubbed version on Netflix with a subscription. Netflix also offers other languages such as Hindi, Telugu, Malayalam, and English for the film. To watch Ghostbusters: Afterlife on Netflix, go to this link [^1^].
-
TamilYogi: TamilYogi is a popular website that offers Tamil dubbed movies for free online. You can watch Ghostbusters: Afterlife in Tamil dubbed version on TamilYogi without any registration or payment. However, be aware that TamilYogi is an illegal website that may contain ads, pop-ups, malware, and viruses. To watch Ghostbusters: Afterlife on TamilYogi, go to this link [^2^].
-
Internet Archive: Internet Archive is a non-profit digital library that preserves and provides access to millions of free books, movies, music, and more. You can watch Ghostbusters: Afterlife in Tamil dubbed version on Internet Archive for free and legally. However, the quality and availability of the film may vary depending on the uploaders. To watch Ghostbusters: Afterlife on Internet Archive, go to this link [^3^].
-
Google Drive: Google Drive is a cloud storage service that allows you to store and share files online. You can watch Ghostbusters: Afterlife in Tamil dubbed version on Google Drive if you have access to a shared folder that contains the film. However, this method may not be legal or safe depending on the source of the file. To watch Ghostbusters: Afterlife on Google Drive, go to this link [^4^].
-
-
We hope this article helps you find the best way to watch Ghostbusters: Afterlife in Tamil dubbed version online. Enjoy the film and remember: don't cross the streams!
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-
-
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diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/utils/virtualenv.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/utils/virtualenv.py
deleted file mode 100644
index c926db4c33215425c838ebf3de02a204f7863e50..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/utils/virtualenv.py
+++ /dev/null
@@ -1,104 +0,0 @@
-import logging
-import os
-import re
-import site
-import sys
-from typing import List, Optional
-
-logger = logging.getLogger(__name__)
-_INCLUDE_SYSTEM_SITE_PACKAGES_REGEX = re.compile(
- r"include-system-site-packages\s*=\s*(?Ptrue|false)"
-)
-
-
-def _running_under_venv() -> bool:
- """Checks if sys.base_prefix and sys.prefix match.
-
- This handles PEP 405 compliant virtual environments.
- """
- return sys.prefix != getattr(sys, "base_prefix", sys.prefix)
-
-
-def _running_under_regular_virtualenv() -> bool:
- """Checks if sys.real_prefix is set.
-
- This handles virtual environments created with pypa's virtualenv.
- """
- # pypa/virtualenv case
- return hasattr(sys, "real_prefix")
-
-
-def running_under_virtualenv() -> bool:
- """Return True if we're running inside a virtualenv, False otherwise."""
- return _running_under_venv() or _running_under_regular_virtualenv()
-
-
-def _get_pyvenv_cfg_lines() -> Optional[List[str]]:
- """Reads {sys.prefix}/pyvenv.cfg and returns its contents as list of lines
-
- Returns None, if it could not read/access the file.
- """
- pyvenv_cfg_file = os.path.join(sys.prefix, "pyvenv.cfg")
- try:
- # Although PEP 405 does not specify, the built-in venv module always
- # writes with UTF-8. (pypa/pip#8717)
- with open(pyvenv_cfg_file, encoding="utf-8") as f:
- return f.read().splitlines() # avoids trailing newlines
- except OSError:
- return None
-
-
-def _no_global_under_venv() -> bool:
- """Check `{sys.prefix}/pyvenv.cfg` for system site-packages inclusion
-
- PEP 405 specifies that when system site-packages are not supposed to be
- visible from a virtual environment, `pyvenv.cfg` must contain the following
- line:
-
- include-system-site-packages = false
-
- Additionally, log a warning if accessing the file fails.
- """
- cfg_lines = _get_pyvenv_cfg_lines()
- if cfg_lines is None:
- # We're not in a "sane" venv, so assume there is no system
- # site-packages access (since that's PEP 405's default state).
- logger.warning(
- "Could not access 'pyvenv.cfg' despite a virtual environment "
- "being active. Assuming global site-packages is not accessible "
- "in this environment."
- )
- return True
-
- for line in cfg_lines:
- match = _INCLUDE_SYSTEM_SITE_PACKAGES_REGEX.match(line)
- if match is not None and match.group("value") == "false":
- return True
- return False
-
-
-def _no_global_under_regular_virtualenv() -> bool:
- """Check if "no-global-site-packages.txt" exists beside site.py
-
- This mirrors logic in pypa/virtualenv for determining whether system
- site-packages are visible in the virtual environment.
- """
- site_mod_dir = os.path.dirname(os.path.abspath(site.__file__))
- no_global_site_packages_file = os.path.join(
- site_mod_dir,
- "no-global-site-packages.txt",
- )
- return os.path.exists(no_global_site_packages_file)
-
-
-def virtualenv_no_global() -> bool:
- """Returns a boolean, whether running in venv with no system site-packages."""
- # PEP 405 compliance needs to be checked first since virtualenv >=20 would
- # return True for both checks, but is only able to use the PEP 405 config.
- if _running_under_venv():
- return _no_global_under_venv()
-
- if _running_under_regular_virtualenv():
- return _no_global_under_regular_virtualenv()
-
- return False
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/distlib/compat.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/distlib/compat.py
deleted file mode 100644
index 1fe3d225acb9bf37acffafc2198dc96c7c7fd313..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/distlib/compat.py
+++ /dev/null
@@ -1,1116 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# Copyright (C) 2013-2017 Vinay Sajip.
-# Licensed to the Python Software Foundation under a contributor agreement.
-# See LICENSE.txt and CONTRIBUTORS.txt.
-#
-from __future__ import absolute_import
-
-import os
-import re
-import sys
-
-try:
- import ssl
-except ImportError: # pragma: no cover
- ssl = None
-
-if sys.version_info[0] < 3: # pragma: no cover
- from StringIO import StringIO
- string_types = basestring,
- text_type = unicode
- from types import FileType as file_type
- import __builtin__ as builtins
- import ConfigParser as configparser
- from urlparse import urlparse, urlunparse, urljoin, urlsplit, urlunsplit
- from urllib import (urlretrieve, quote as _quote, unquote, url2pathname,
- pathname2url, ContentTooShortError, splittype)
-
- def quote(s):
- if isinstance(s, unicode):
- s = s.encode('utf-8')
- return _quote(s)
-
- import urllib2
- from urllib2 import (Request, urlopen, URLError, HTTPError,
- HTTPBasicAuthHandler, HTTPPasswordMgr,
- HTTPHandler, HTTPRedirectHandler,
- build_opener)
- if ssl:
- from urllib2 import HTTPSHandler
- import httplib
- import xmlrpclib
- import Queue as queue
- from HTMLParser import HTMLParser
- import htmlentitydefs
- raw_input = raw_input
- from itertools import ifilter as filter
- from itertools import ifilterfalse as filterfalse
-
- # Leaving this around for now, in case it needs resurrecting in some way
- # _userprog = None
- # def splituser(host):
- # """splituser('user[:passwd]@host[:port]') --> 'user[:passwd]', 'host[:port]'."""
- # global _userprog
- # if _userprog is None:
- # import re
- # _userprog = re.compile('^(.*)@(.*)$')
-
- # match = _userprog.match(host)
- # if match: return match.group(1, 2)
- # return None, host
-
-else: # pragma: no cover
- from io import StringIO
- string_types = str,
- text_type = str
- from io import TextIOWrapper as file_type
- import builtins
- import configparser
- import shutil
- from urllib.parse import (urlparse, urlunparse, urljoin, quote,
- unquote, urlsplit, urlunsplit, splittype)
- from urllib.request import (urlopen, urlretrieve, Request, url2pathname,
- pathname2url,
- HTTPBasicAuthHandler, HTTPPasswordMgr,
- HTTPHandler, HTTPRedirectHandler,
- build_opener)
- if ssl:
- from urllib.request import HTTPSHandler
- from urllib.error import HTTPError, URLError, ContentTooShortError
- import http.client as httplib
- import urllib.request as urllib2
- import xmlrpc.client as xmlrpclib
- import queue
- from html.parser import HTMLParser
- import html.entities as htmlentitydefs
- raw_input = input
- from itertools import filterfalse
- filter = filter
-
-
-try:
- from ssl import match_hostname, CertificateError
-except ImportError: # pragma: no cover
- class CertificateError(ValueError):
- pass
-
-
- def _dnsname_match(dn, hostname, max_wildcards=1):
- """Matching according to RFC 6125, section 6.4.3
-
- http://tools.ietf.org/html/rfc6125#section-6.4.3
- """
- pats = []
- if not dn:
- return False
-
- parts = dn.split('.')
- leftmost, remainder = parts[0], parts[1:]
-
- wildcards = leftmost.count('*')
- if wildcards > max_wildcards:
- # Issue #17980: avoid denials of service by refusing more
- # than one wildcard per fragment. A survey of established
- # policy among SSL implementations showed it to be a
- # reasonable choice.
- raise CertificateError(
- "too many wildcards in certificate DNS name: " + repr(dn))
-
- # speed up common case w/o wildcards
- if not wildcards:
- return dn.lower() == hostname.lower()
-
- # RFC 6125, section 6.4.3, subitem 1.
- # The client SHOULD NOT attempt to match a presented identifier in which
- # the wildcard character comprises a label other than the left-most label.
- if leftmost == '*':
- # When '*' is a fragment by itself, it matches a non-empty dotless
- # fragment.
- pats.append('[^.]+')
- elif leftmost.startswith('xn--') or hostname.startswith('xn--'):
- # RFC 6125, section 6.4.3, subitem 3.
- # The client SHOULD NOT attempt to match a presented identifier
- # where the wildcard character is embedded within an A-label or
- # U-label of an internationalized domain name.
- pats.append(re.escape(leftmost))
- else:
- # Otherwise, '*' matches any dotless string, e.g. www*
- pats.append(re.escape(leftmost).replace(r'\*', '[^.]*'))
-
- # add the remaining fragments, ignore any wildcards
- for frag in remainder:
- pats.append(re.escape(frag))
-
- pat = re.compile(r'\A' + r'\.'.join(pats) + r'\Z', re.IGNORECASE)
- return pat.match(hostname)
-
-
- def match_hostname(cert, hostname):
- """Verify that *cert* (in decoded format as returned by
- SSLSocket.getpeercert()) matches the *hostname*. RFC 2818 and RFC 6125
- rules are followed, but IP addresses are not accepted for *hostname*.
-
- CertificateError is raised on failure. On success, the function
- returns nothing.
- """
- if not cert:
- raise ValueError("empty or no certificate, match_hostname needs a "
- "SSL socket or SSL context with either "
- "CERT_OPTIONAL or CERT_REQUIRED")
- dnsnames = []
- san = cert.get('subjectAltName', ())
- for key, value in san:
- if key == 'DNS':
- if _dnsname_match(value, hostname):
- return
- dnsnames.append(value)
- if not dnsnames:
- # The subject is only checked when there is no dNSName entry
- # in subjectAltName
- for sub in cert.get('subject', ()):
- for key, value in sub:
- # XXX according to RFC 2818, the most specific Common Name
- # must be used.
- if key == 'commonName':
- if _dnsname_match(value, hostname):
- return
- dnsnames.append(value)
- if len(dnsnames) > 1:
- raise CertificateError("hostname %r "
- "doesn't match either of %s"
- % (hostname, ', '.join(map(repr, dnsnames))))
- elif len(dnsnames) == 1:
- raise CertificateError("hostname %r "
- "doesn't match %r"
- % (hostname, dnsnames[0]))
- else:
- raise CertificateError("no appropriate commonName or "
- "subjectAltName fields were found")
-
-
-try:
- from types import SimpleNamespace as Container
-except ImportError: # pragma: no cover
- class Container(object):
- """
- A generic container for when multiple values need to be returned
- """
- def __init__(self, **kwargs):
- self.__dict__.update(kwargs)
-
-
-try:
- from shutil import which
-except ImportError: # pragma: no cover
- # Implementation from Python 3.3
- def which(cmd, mode=os.F_OK | os.X_OK, path=None):
- """Given a command, mode, and a PATH string, return the path which
- conforms to the given mode on the PATH, or None if there is no such
- file.
-
- `mode` defaults to os.F_OK | os.X_OK. `path` defaults to the result
- of os.environ.get("PATH"), or can be overridden with a custom search
- path.
-
- """
- # Check that a given file can be accessed with the correct mode.
- # Additionally check that `file` is not a directory, as on Windows
- # directories pass the os.access check.
- def _access_check(fn, mode):
- return (os.path.exists(fn) and os.access(fn, mode)
- and not os.path.isdir(fn))
-
- # If we're given a path with a directory part, look it up directly rather
- # than referring to PATH directories. This includes checking relative to the
- # current directory, e.g. ./script
- if os.path.dirname(cmd):
- if _access_check(cmd, mode):
- return cmd
- return None
-
- if path is None:
- path = os.environ.get("PATH", os.defpath)
- if not path:
- return None
- path = path.split(os.pathsep)
-
- if sys.platform == "win32":
- # The current directory takes precedence on Windows.
- if not os.curdir in path:
- path.insert(0, os.curdir)
-
- # PATHEXT is necessary to check on Windows.
- pathext = os.environ.get("PATHEXT", "").split(os.pathsep)
- # See if the given file matches any of the expected path extensions.
- # This will allow us to short circuit when given "python.exe".
- # If it does match, only test that one, otherwise we have to try
- # others.
- if any(cmd.lower().endswith(ext.lower()) for ext in pathext):
- files = [cmd]
- else:
- files = [cmd + ext for ext in pathext]
- else:
- # On other platforms you don't have things like PATHEXT to tell you
- # what file suffixes are executable, so just pass on cmd as-is.
- files = [cmd]
-
- seen = set()
- for dir in path:
- normdir = os.path.normcase(dir)
- if not normdir in seen:
- seen.add(normdir)
- for thefile in files:
- name = os.path.join(dir, thefile)
- if _access_check(name, mode):
- return name
- return None
-
-
-# ZipFile is a context manager in 2.7, but not in 2.6
-
-from zipfile import ZipFile as BaseZipFile
-
-if hasattr(BaseZipFile, '__enter__'): # pragma: no cover
- ZipFile = BaseZipFile
-else: # pragma: no cover
- from zipfile import ZipExtFile as BaseZipExtFile
-
- class ZipExtFile(BaseZipExtFile):
- def __init__(self, base):
- self.__dict__.update(base.__dict__)
-
- def __enter__(self):
- return self
-
- def __exit__(self, *exc_info):
- self.close()
- # return None, so if an exception occurred, it will propagate
-
- class ZipFile(BaseZipFile):
- def __enter__(self):
- return self
-
- def __exit__(self, *exc_info):
- self.close()
- # return None, so if an exception occurred, it will propagate
-
- def open(self, *args, **kwargs):
- base = BaseZipFile.open(self, *args, **kwargs)
- return ZipExtFile(base)
-
-try:
- from platform import python_implementation
-except ImportError: # pragma: no cover
- def python_implementation():
- """Return a string identifying the Python implementation."""
- if 'PyPy' in sys.version:
- return 'PyPy'
- if os.name == 'java':
- return 'Jython'
- if sys.version.startswith('IronPython'):
- return 'IronPython'
- return 'CPython'
-
-import shutil
-import sysconfig
-
-try:
- callable = callable
-except NameError: # pragma: no cover
- from collections.abc import Callable
-
- def callable(obj):
- return isinstance(obj, Callable)
-
-
-try:
- fsencode = os.fsencode
- fsdecode = os.fsdecode
-except AttributeError: # pragma: no cover
- # Issue #99: on some systems (e.g. containerised),
- # sys.getfilesystemencoding() returns None, and we need a real value,
- # so fall back to utf-8. From the CPython 2.7 docs relating to Unix and
- # sys.getfilesystemencoding(): the return value is "the user’s preference
- # according to the result of nl_langinfo(CODESET), or None if the
- # nl_langinfo(CODESET) failed."
- _fsencoding = sys.getfilesystemencoding() or 'utf-8'
- if _fsencoding == 'mbcs':
- _fserrors = 'strict'
- else:
- _fserrors = 'surrogateescape'
-
- def fsencode(filename):
- if isinstance(filename, bytes):
- return filename
- elif isinstance(filename, text_type):
- return filename.encode(_fsencoding, _fserrors)
- else:
- raise TypeError("expect bytes or str, not %s" %
- type(filename).__name__)
-
- def fsdecode(filename):
- if isinstance(filename, text_type):
- return filename
- elif isinstance(filename, bytes):
- return filename.decode(_fsencoding, _fserrors)
- else:
- raise TypeError("expect bytes or str, not %s" %
- type(filename).__name__)
-
-try:
- from tokenize import detect_encoding
-except ImportError: # pragma: no cover
- from codecs import BOM_UTF8, lookup
- import re
-
- cookie_re = re.compile(r"coding[:=]\s*([-\w.]+)")
-
- def _get_normal_name(orig_enc):
- """Imitates get_normal_name in tokenizer.c."""
- # Only care about the first 12 characters.
- enc = orig_enc[:12].lower().replace("_", "-")
- if enc == "utf-8" or enc.startswith("utf-8-"):
- return "utf-8"
- if enc in ("latin-1", "iso-8859-1", "iso-latin-1") or \
- enc.startswith(("latin-1-", "iso-8859-1-", "iso-latin-1-")):
- return "iso-8859-1"
- return orig_enc
-
- def detect_encoding(readline):
- """
- The detect_encoding() function is used to detect the encoding that should
- be used to decode a Python source file. It requires one argument, readline,
- in the same way as the tokenize() generator.
-
- It will call readline a maximum of twice, and return the encoding used
- (as a string) and a list of any lines (left as bytes) it has read in.
-
- It detects the encoding from the presence of a utf-8 bom or an encoding
- cookie as specified in pep-0263. If both a bom and a cookie are present,
- but disagree, a SyntaxError will be raised. If the encoding cookie is an
- invalid charset, raise a SyntaxError. Note that if a utf-8 bom is found,
- 'utf-8-sig' is returned.
-
- If no encoding is specified, then the default of 'utf-8' will be returned.
- """
- try:
- filename = readline.__self__.name
- except AttributeError:
- filename = None
- bom_found = False
- encoding = None
- default = 'utf-8'
- def read_or_stop():
- try:
- return readline()
- except StopIteration:
- return b''
-
- def find_cookie(line):
- try:
- # Decode as UTF-8. Either the line is an encoding declaration,
- # in which case it should be pure ASCII, or it must be UTF-8
- # per default encoding.
- line_string = line.decode('utf-8')
- except UnicodeDecodeError:
- msg = "invalid or missing encoding declaration"
- if filename is not None:
- msg = '{} for {!r}'.format(msg, filename)
- raise SyntaxError(msg)
-
- matches = cookie_re.findall(line_string)
- if not matches:
- return None
- encoding = _get_normal_name(matches[0])
- try:
- codec = lookup(encoding)
- except LookupError:
- # This behaviour mimics the Python interpreter
- if filename is None:
- msg = "unknown encoding: " + encoding
- else:
- msg = "unknown encoding for {!r}: {}".format(filename,
- encoding)
- raise SyntaxError(msg)
-
- if bom_found:
- if codec.name != 'utf-8':
- # This behaviour mimics the Python interpreter
- if filename is None:
- msg = 'encoding problem: utf-8'
- else:
- msg = 'encoding problem for {!r}: utf-8'.format(filename)
- raise SyntaxError(msg)
- encoding += '-sig'
- return encoding
-
- first = read_or_stop()
- if first.startswith(BOM_UTF8):
- bom_found = True
- first = first[3:]
- default = 'utf-8-sig'
- if not first:
- return default, []
-
- encoding = find_cookie(first)
- if encoding:
- return encoding, [first]
-
- second = read_or_stop()
- if not second:
- return default, [first]
-
- encoding = find_cookie(second)
- if encoding:
- return encoding, [first, second]
-
- return default, [first, second]
-
-# For converting & <-> & etc.
-try:
- from html import escape
-except ImportError:
- from cgi import escape
-if sys.version_info[:2] < (3, 4):
- unescape = HTMLParser().unescape
-else:
- from html import unescape
-
-try:
- from collections import ChainMap
-except ImportError: # pragma: no cover
- from collections import MutableMapping
-
- try:
- from reprlib import recursive_repr as _recursive_repr
- except ImportError:
- def _recursive_repr(fillvalue='...'):
- '''
- Decorator to make a repr function return fillvalue for a recursive
- call
- '''
-
- def decorating_function(user_function):
- repr_running = set()
-
- def wrapper(self):
- key = id(self), get_ident()
- if key in repr_running:
- return fillvalue
- repr_running.add(key)
- try:
- result = user_function(self)
- finally:
- repr_running.discard(key)
- return result
-
- # Can't use functools.wraps() here because of bootstrap issues
- wrapper.__module__ = getattr(user_function, '__module__')
- wrapper.__doc__ = getattr(user_function, '__doc__')
- wrapper.__name__ = getattr(user_function, '__name__')
- wrapper.__annotations__ = getattr(user_function, '__annotations__', {})
- return wrapper
-
- return decorating_function
-
- class ChainMap(MutableMapping):
- ''' A ChainMap groups multiple dicts (or other mappings) together
- to create a single, updateable view.
-
- The underlying mappings are stored in a list. That list is public and can
- accessed or updated using the *maps* attribute. There is no other state.
-
- Lookups search the underlying mappings successively until a key is found.
- In contrast, writes, updates, and deletions only operate on the first
- mapping.
-
- '''
-
- def __init__(self, *maps):
- '''Initialize a ChainMap by setting *maps* to the given mappings.
- If no mappings are provided, a single empty dictionary is used.
-
- '''
- self.maps = list(maps) or [{}] # always at least one map
-
- def __missing__(self, key):
- raise KeyError(key)
-
- def __getitem__(self, key):
- for mapping in self.maps:
- try:
- return mapping[key] # can't use 'key in mapping' with defaultdict
- except KeyError:
- pass
- return self.__missing__(key) # support subclasses that define __missing__
-
- def get(self, key, default=None):
- return self[key] if key in self else default
-
- def __len__(self):
- return len(set().union(*self.maps)) # reuses stored hash values if possible
-
- def __iter__(self):
- return iter(set().union(*self.maps))
-
- def __contains__(self, key):
- return any(key in m for m in self.maps)
-
- def __bool__(self):
- return any(self.maps)
-
- @_recursive_repr()
- def __repr__(self):
- return '{0.__class__.__name__}({1})'.format(
- self, ', '.join(map(repr, self.maps)))
-
- @classmethod
- def fromkeys(cls, iterable, *args):
- 'Create a ChainMap with a single dict created from the iterable.'
- return cls(dict.fromkeys(iterable, *args))
-
- def copy(self):
- 'New ChainMap or subclass with a new copy of maps[0] and refs to maps[1:]'
- return self.__class__(self.maps[0].copy(), *self.maps[1:])
-
- __copy__ = copy
-
- def new_child(self): # like Django's Context.push()
- 'New ChainMap with a new dict followed by all previous maps.'
- return self.__class__({}, *self.maps)
-
- @property
- def parents(self): # like Django's Context.pop()
- 'New ChainMap from maps[1:].'
- return self.__class__(*self.maps[1:])
-
- def __setitem__(self, key, value):
- self.maps[0][key] = value
-
- def __delitem__(self, key):
- try:
- del self.maps[0][key]
- except KeyError:
- raise KeyError('Key not found in the first mapping: {!r}'.format(key))
-
- def popitem(self):
- 'Remove and return an item pair from maps[0]. Raise KeyError is maps[0] is empty.'
- try:
- return self.maps[0].popitem()
- except KeyError:
- raise KeyError('No keys found in the first mapping.')
-
- def pop(self, key, *args):
- 'Remove *key* from maps[0] and return its value. Raise KeyError if *key* not in maps[0].'
- try:
- return self.maps[0].pop(key, *args)
- except KeyError:
- raise KeyError('Key not found in the first mapping: {!r}'.format(key))
-
- def clear(self):
- 'Clear maps[0], leaving maps[1:] intact.'
- self.maps[0].clear()
-
-try:
- from importlib.util import cache_from_source # Python >= 3.4
-except ImportError: # pragma: no cover
- def cache_from_source(path, debug_override=None):
- assert path.endswith('.py')
- if debug_override is None:
- debug_override = __debug__
- if debug_override:
- suffix = 'c'
- else:
- suffix = 'o'
- return path + suffix
-
-try:
- from collections import OrderedDict
-except ImportError: # pragma: no cover
-## {{{ http://code.activestate.com/recipes/576693/ (r9)
-# Backport of OrderedDict() class that runs on Python 2.4, 2.5, 2.6, 2.7 and pypy.
-# Passes Python2.7's test suite and incorporates all the latest updates.
- try:
- from thread import get_ident as _get_ident
- except ImportError:
- from dummy_thread import get_ident as _get_ident
-
- try:
- from _abcoll import KeysView, ValuesView, ItemsView
- except ImportError:
- pass
-
-
- class OrderedDict(dict):
- 'Dictionary that remembers insertion order'
- # An inherited dict maps keys to values.
- # The inherited dict provides __getitem__, __len__, __contains__, and get.
- # The remaining methods are order-aware.
- # Big-O running times for all methods are the same as for regular dictionaries.
-
- # The internal self.__map dictionary maps keys to links in a doubly linked list.
- # The circular doubly linked list starts and ends with a sentinel element.
- # The sentinel element never gets deleted (this simplifies the algorithm).
- # Each link is stored as a list of length three: [PREV, NEXT, KEY].
-
- def __init__(self, *args, **kwds):
- '''Initialize an ordered dictionary. Signature is the same as for
- regular dictionaries, but keyword arguments are not recommended
- because their insertion order is arbitrary.
-
- '''
- if len(args) > 1:
- raise TypeError('expected at most 1 arguments, got %d' % len(args))
- try:
- self.__root
- except AttributeError:
- self.__root = root = [] # sentinel node
- root[:] = [root, root, None]
- self.__map = {}
- self.__update(*args, **kwds)
-
- def __setitem__(self, key, value, dict_setitem=dict.__setitem__):
- 'od.__setitem__(i, y) <==> od[i]=y'
- # Setting a new item creates a new link which goes at the end of the linked
- # list, and the inherited dictionary is updated with the new key/value pair.
- if key not in self:
- root = self.__root
- last = root[0]
- last[1] = root[0] = self.__map[key] = [last, root, key]
- dict_setitem(self, key, value)
-
- def __delitem__(self, key, dict_delitem=dict.__delitem__):
- 'od.__delitem__(y) <==> del od[y]'
- # Deleting an existing item uses self.__map to find the link which is
- # then removed by updating the links in the predecessor and successor nodes.
- dict_delitem(self, key)
- link_prev, link_next, key = self.__map.pop(key)
- link_prev[1] = link_next
- link_next[0] = link_prev
-
- def __iter__(self):
- 'od.__iter__() <==> iter(od)'
- root = self.__root
- curr = root[1]
- while curr is not root:
- yield curr[2]
- curr = curr[1]
-
- def __reversed__(self):
- 'od.__reversed__() <==> reversed(od)'
- root = self.__root
- curr = root[0]
- while curr is not root:
- yield curr[2]
- curr = curr[0]
-
- def clear(self):
- 'od.clear() -> None. Remove all items from od.'
- try:
- for node in self.__map.itervalues():
- del node[:]
- root = self.__root
- root[:] = [root, root, None]
- self.__map.clear()
- except AttributeError:
- pass
- dict.clear(self)
-
- def popitem(self, last=True):
- '''od.popitem() -> (k, v), return and remove a (key, value) pair.
- Pairs are returned in LIFO order if last is true or FIFO order if false.
-
- '''
- if not self:
- raise KeyError('dictionary is empty')
- root = self.__root
- if last:
- link = root[0]
- link_prev = link[0]
- link_prev[1] = root
- root[0] = link_prev
- else:
- link = root[1]
- link_next = link[1]
- root[1] = link_next
- link_next[0] = root
- key = link[2]
- del self.__map[key]
- value = dict.pop(self, key)
- return key, value
-
- # -- the following methods do not depend on the internal structure --
-
- def keys(self):
- 'od.keys() -> list of keys in od'
- return list(self)
-
- def values(self):
- 'od.values() -> list of values in od'
- return [self[key] for key in self]
-
- def items(self):
- 'od.items() -> list of (key, value) pairs in od'
- return [(key, self[key]) for key in self]
-
- def iterkeys(self):
- 'od.iterkeys() -> an iterator over the keys in od'
- return iter(self)
-
- def itervalues(self):
- 'od.itervalues -> an iterator over the values in od'
- for k in self:
- yield self[k]
-
- def iteritems(self):
- 'od.iteritems -> an iterator over the (key, value) items in od'
- for k in self:
- yield (k, self[k])
-
- def update(*args, **kwds):
- '''od.update(E, **F) -> None. Update od from dict/iterable E and F.
-
- If E is a dict instance, does: for k in E: od[k] = E[k]
- If E has a .keys() method, does: for k in E.keys(): od[k] = E[k]
- Or if E is an iterable of items, does: for k, v in E: od[k] = v
- In either case, this is followed by: for k, v in F.items(): od[k] = v
-
- '''
- if len(args) > 2:
- raise TypeError('update() takes at most 2 positional '
- 'arguments (%d given)' % (len(args),))
- elif not args:
- raise TypeError('update() takes at least 1 argument (0 given)')
- self = args[0]
- # Make progressively weaker assumptions about "other"
- other = ()
- if len(args) == 2:
- other = args[1]
- if isinstance(other, dict):
- for key in other:
- self[key] = other[key]
- elif hasattr(other, 'keys'):
- for key in other.keys():
- self[key] = other[key]
- else:
- for key, value in other:
- self[key] = value
- for key, value in kwds.items():
- self[key] = value
-
- __update = update # let subclasses override update without breaking __init__
-
- __marker = object()
-
- def pop(self, key, default=__marker):
- '''od.pop(k[,d]) -> v, remove specified key and return the corresponding value.
- If key is not found, d is returned if given, otherwise KeyError is raised.
-
- '''
- if key in self:
- result = self[key]
- del self[key]
- return result
- if default is self.__marker:
- raise KeyError(key)
- return default
-
- def setdefault(self, key, default=None):
- 'od.setdefault(k[,d]) -> od.get(k,d), also set od[k]=d if k not in od'
- if key in self:
- return self[key]
- self[key] = default
- return default
-
- def __repr__(self, _repr_running=None):
- 'od.__repr__() <==> repr(od)'
- if not _repr_running: _repr_running = {}
- call_key = id(self), _get_ident()
- if call_key in _repr_running:
- return '...'
- _repr_running[call_key] = 1
- try:
- if not self:
- return '%s()' % (self.__class__.__name__,)
- return '%s(%r)' % (self.__class__.__name__, self.items())
- finally:
- del _repr_running[call_key]
-
- def __reduce__(self):
- 'Return state information for pickling'
- items = [[k, self[k]] for k in self]
- inst_dict = vars(self).copy()
- for k in vars(OrderedDict()):
- inst_dict.pop(k, None)
- if inst_dict:
- return (self.__class__, (items,), inst_dict)
- return self.__class__, (items,)
-
- def copy(self):
- 'od.copy() -> a shallow copy of od'
- return self.__class__(self)
-
- @classmethod
- def fromkeys(cls, iterable, value=None):
- '''OD.fromkeys(S[, v]) -> New ordered dictionary with keys from S
- and values equal to v (which defaults to None).
-
- '''
- d = cls()
- for key in iterable:
- d[key] = value
- return d
-
- def __eq__(self, other):
- '''od.__eq__(y) <==> od==y. Comparison to another OD is order-sensitive
- while comparison to a regular mapping is order-insensitive.
-
- '''
- if isinstance(other, OrderedDict):
- return len(self)==len(other) and self.items() == other.items()
- return dict.__eq__(self, other)
-
- def __ne__(self, other):
- return not self == other
-
- # -- the following methods are only used in Python 2.7 --
-
- def viewkeys(self):
- "od.viewkeys() -> a set-like object providing a view on od's keys"
- return KeysView(self)
-
- def viewvalues(self):
- "od.viewvalues() -> an object providing a view on od's values"
- return ValuesView(self)
-
- def viewitems(self):
- "od.viewitems() -> a set-like object providing a view on od's items"
- return ItemsView(self)
-
-try:
- from logging.config import BaseConfigurator, valid_ident
-except ImportError: # pragma: no cover
- IDENTIFIER = re.compile('^[a-z_][a-z0-9_]*$', re.I)
-
-
- def valid_ident(s):
- m = IDENTIFIER.match(s)
- if not m:
- raise ValueError('Not a valid Python identifier: %r' % s)
- return True
-
-
- # The ConvertingXXX classes are wrappers around standard Python containers,
- # and they serve to convert any suitable values in the container. The
- # conversion converts base dicts, lists and tuples to their wrapped
- # equivalents, whereas strings which match a conversion format are converted
- # appropriately.
- #
- # Each wrapper should have a configurator attribute holding the actual
- # configurator to use for conversion.
-
- class ConvertingDict(dict):
- """A converting dictionary wrapper."""
-
- def __getitem__(self, key):
- value = dict.__getitem__(self, key)
- result = self.configurator.convert(value)
- #If the converted value is different, save for next time
- if value is not result:
- self[key] = result
- if type(result) in (ConvertingDict, ConvertingList,
- ConvertingTuple):
- result.parent = self
- result.key = key
- return result
-
- def get(self, key, default=None):
- value = dict.get(self, key, default)
- result = self.configurator.convert(value)
- #If the converted value is different, save for next time
- if value is not result:
- self[key] = result
- if type(result) in (ConvertingDict, ConvertingList,
- ConvertingTuple):
- result.parent = self
- result.key = key
- return result
-
- def pop(self, key, default=None):
- value = dict.pop(self, key, default)
- result = self.configurator.convert(value)
- if value is not result:
- if type(result) in (ConvertingDict, ConvertingList,
- ConvertingTuple):
- result.parent = self
- result.key = key
- return result
-
- class ConvertingList(list):
- """A converting list wrapper."""
- def __getitem__(self, key):
- value = list.__getitem__(self, key)
- result = self.configurator.convert(value)
- #If the converted value is different, save for next time
- if value is not result:
- self[key] = result
- if type(result) in (ConvertingDict, ConvertingList,
- ConvertingTuple):
- result.parent = self
- result.key = key
- return result
-
- def pop(self, idx=-1):
- value = list.pop(self, idx)
- result = self.configurator.convert(value)
- if value is not result:
- if type(result) in (ConvertingDict, ConvertingList,
- ConvertingTuple):
- result.parent = self
- return result
-
- class ConvertingTuple(tuple):
- """A converting tuple wrapper."""
- def __getitem__(self, key):
- value = tuple.__getitem__(self, key)
- result = self.configurator.convert(value)
- if value is not result:
- if type(result) in (ConvertingDict, ConvertingList,
- ConvertingTuple):
- result.parent = self
- result.key = key
- return result
-
- class BaseConfigurator(object):
- """
- The configurator base class which defines some useful defaults.
- """
-
- CONVERT_PATTERN = re.compile(r'^(?P[a-z]+)://(?P.*)$')
-
- WORD_PATTERN = re.compile(r'^\s*(\w+)\s*')
- DOT_PATTERN = re.compile(r'^\.\s*(\w+)\s*')
- INDEX_PATTERN = re.compile(r'^\[\s*(\w+)\s*\]\s*')
- DIGIT_PATTERN = re.compile(r'^\d+$')
-
- value_converters = {
- 'ext' : 'ext_convert',
- 'cfg' : 'cfg_convert',
- }
-
- # We might want to use a different one, e.g. importlib
- importer = staticmethod(__import__)
-
- def __init__(self, config):
- self.config = ConvertingDict(config)
- self.config.configurator = self
-
- def resolve(self, s):
- """
- Resolve strings to objects using standard import and attribute
- syntax.
- """
- name = s.split('.')
- used = name.pop(0)
- try:
- found = self.importer(used)
- for frag in name:
- used += '.' + frag
- try:
- found = getattr(found, frag)
- except AttributeError:
- self.importer(used)
- found = getattr(found, frag)
- return found
- except ImportError:
- e, tb = sys.exc_info()[1:]
- v = ValueError('Cannot resolve %r: %s' % (s, e))
- v.__cause__, v.__traceback__ = e, tb
- raise v
-
- def ext_convert(self, value):
- """Default converter for the ext:// protocol."""
- return self.resolve(value)
-
- def cfg_convert(self, value):
- """Default converter for the cfg:// protocol."""
- rest = value
- m = self.WORD_PATTERN.match(rest)
- if m is None:
- raise ValueError("Unable to convert %r" % value)
- else:
- rest = rest[m.end():]
- d = self.config[m.groups()[0]]
- #print d, rest
- while rest:
- m = self.DOT_PATTERN.match(rest)
- if m:
- d = d[m.groups()[0]]
- else:
- m = self.INDEX_PATTERN.match(rest)
- if m:
- idx = m.groups()[0]
- if not self.DIGIT_PATTERN.match(idx):
- d = d[idx]
- else:
- try:
- n = int(idx) # try as number first (most likely)
- d = d[n]
- except TypeError:
- d = d[idx]
- if m:
- rest = rest[m.end():]
- else:
- raise ValueError('Unable to convert '
- '%r at %r' % (value, rest))
- #rest should be empty
- return d
-
- def convert(self, value):
- """
- Convert values to an appropriate type. dicts, lists and tuples are
- replaced by their converting alternatives. Strings are checked to
- see if they have a conversion format and are converted if they do.
- """
- if not isinstance(value, ConvertingDict) and isinstance(value, dict):
- value = ConvertingDict(value)
- value.configurator = self
- elif not isinstance(value, ConvertingList) and isinstance(value, list):
- value = ConvertingList(value)
- value.configurator = self
- elif not isinstance(value, ConvertingTuple) and\
- isinstance(value, tuple):
- value = ConvertingTuple(value)
- value.configurator = self
- elif isinstance(value, string_types):
- m = self.CONVERT_PATTERN.match(value)
- if m:
- d = m.groupdict()
- prefix = d['prefix']
- converter = self.value_converters.get(prefix, None)
- if converter:
- suffix = d['suffix']
- converter = getattr(self, converter)
- value = converter(suffix)
- return value
-
- def configure_custom(self, config):
- """Configure an object with a user-supplied factory."""
- c = config.pop('()')
- if not callable(c):
- c = self.resolve(c)
- props = config.pop('.', None)
- # Check for valid identifiers
- kwargs = dict([(k, config[k]) for k in config if valid_ident(k)])
- result = c(**kwargs)
- if props:
- for name, value in props.items():
- setattr(result, name, value)
- return result
-
- def as_tuple(self, value):
- """Utility function which converts lists to tuples."""
- if isinstance(value, list):
- value = tuple(value)
- return value
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/platformdirs/__init__.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/platformdirs/__init__.py
deleted file mode 100644
index 9d513dcf17767c6f80d721cb4191decc7633571c..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/platformdirs/__init__.py
+++ /dev/null
@@ -1,340 +0,0 @@
-"""
-Utilities for determining application-specific dirs. See for details and
-usage.
-"""
-from __future__ import annotations
-
-import os
-import sys
-from pathlib import Path
-from typing import TYPE_CHECKING
-
-if TYPE_CHECKING:
- from pip._vendor.typing_extensions import Literal # pragma: no cover
-
-from .api import PlatformDirsABC
-from .version import __version__, __version_info__
-
-
-def _set_platform_dir_class() -> type[PlatformDirsABC]:
- if sys.platform == "win32":
- from pip._vendor.platformdirs.windows import Windows as Result
- elif sys.platform == "darwin":
- from pip._vendor.platformdirs.macos import MacOS as Result
- else:
- from pip._vendor.platformdirs.unix import Unix as Result
-
- if os.getenv("ANDROID_DATA") == "/data" and os.getenv("ANDROID_ROOT") == "/system":
-
- if os.getenv("SHELL") is not None:
- return Result
-
- from pip._vendor.platformdirs.android import _android_folder
-
- if _android_folder() is not None:
- from pip._vendor.platformdirs.android import Android
-
- return Android # return to avoid redefinition of result
-
- return Result
-
-
-PlatformDirs = _set_platform_dir_class() #: Currently active platform
-AppDirs = PlatformDirs #: Backwards compatibility with appdirs
-
-
-def user_data_dir(
- appname: str | None = None,
- appauthor: str | None | Literal[False] = None,
- version: str | None = None,
- roaming: bool = False,
-) -> str:
- """
- :param appname: See `appname `.
- :param appauthor: See `appauthor `.
- :param version: See `version `.
- :param roaming: See `roaming `.
- :returns: data directory tied to the user
- """
- return PlatformDirs(appname=appname, appauthor=appauthor, version=version, roaming=roaming).user_data_dir
-
-
-def site_data_dir(
- appname: str | None = None,
- appauthor: str | None | Literal[False] = None,
- version: str | None = None,
- multipath: bool = False,
-) -> str:
- """
- :param appname: See `appname `.
- :param appauthor: See `appauthor `.
- :param version: See `version `.
- :param multipath: See `roaming `.
- :returns: data directory shared by users
- """
- return PlatformDirs(appname=appname, appauthor=appauthor, version=version, multipath=multipath).site_data_dir
-
-
-def user_config_dir(
- appname: str | None = None,
- appauthor: str | None | Literal[False] = None,
- version: str | None = None,
- roaming: bool = False,
-) -> str:
- """
- :param appname: See `appname `.
- :param appauthor: See `appauthor `.
- :param version: See `version `.
- :param roaming: See `roaming `.
- :returns: config directory tied to the user
- """
- return PlatformDirs(appname=appname, appauthor=appauthor, version=version, roaming=roaming).user_config_dir
-
-
-def site_config_dir(
- appname: str | None = None,
- appauthor: str | None | Literal[False] = None,
- version: str | None = None,
- multipath: bool = False,
-) -> str:
- """
- :param appname: See `appname `.
- :param appauthor: See `appauthor `.
- :param version: See `version `.
- :param multipath: See `roaming `.
- :returns: config directory shared by the users
- """
- return PlatformDirs(appname=appname, appauthor=appauthor, version=version, multipath=multipath).site_config_dir
-
-
-def user_cache_dir(
- appname: str | None = None,
- appauthor: str | None | Literal[False] = None,
- version: str | None = None,
- opinion: bool = True,
-) -> str:
- """
- :param appname: See `appname `.
- :param appauthor: See `appauthor `.
- :param version: See `version `.
- :param opinion: See `roaming `.
- :returns: cache directory tied to the user
- """
- return PlatformDirs(appname=appname, appauthor=appauthor, version=version, opinion=opinion).user_cache_dir
-
-
-def user_state_dir(
- appname: str | None = None,
- appauthor: str | None | Literal[False] = None,
- version: str | None = None,
- roaming: bool = False,
-) -> str:
- """
- :param appname: See `appname `.
- :param appauthor: See `appauthor `.
- :param version: See `version `.
- :param roaming: See `roaming `.
- :returns: state directory tied to the user
- """
- return PlatformDirs(appname=appname, appauthor=appauthor, version=version, roaming=roaming).user_state_dir
-
-
-def user_log_dir(
- appname: str | None = None,
- appauthor: str | None | Literal[False] = None,
- version: str | None = None,
- opinion: bool = True,
-) -> str:
- """
- :param appname: See `appname `.
- :param appauthor: See `appauthor `.
- :param version: See `version
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--- - -## Inpainting - -For inpainting, you need an initial image, a mask image, and a prompt describing what to replace the mask with. ControlNet models allow you to add another control image to condition a model with. Let’s condition the model with a canny image, a white outline of an image on a black background. This way, the ControlNet can use the canny image as a control to guide the model to generate an image with the same outline. - -Load an initial image and a mask image: - -```py -from diffusers import StableDiffusionControlNetInpaintPipeline, ControlNetModel, UniPCMultistepScheduler -from diffusers.utils import load_image -import numpy as np -import torch - -init_image = load_image( - "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet-inpaint.jpg" -) -init_image = init_image.resize((512, 512)) - -mask_image = load_image( - "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet-inpaint-mask.jpg" -) -mask_image = mask_image.resize((512, 512)) -``` - -Create a function to prepare the control image from the initial and mask images. This'll create a tensor to mark the pixels in `init_image` as masked if the corresponding pixel in `mask_image` is over a certain threshold. - -```py -def make_inpaint_condition(image, image_mask): - image = np.array(image.convert("RGB")).astype(np.float32) / 255.0 - image_mask = np.array(image_mask.convert("L")).astype(np.float32) / 255.0 - - assert image.shape[0:1] == image_mask.shape[0:1] - image[image_mask > 0.5] = 1.0 # set as masked pixel - image = np.expand_dims(image, 0).transpose(0, 3, 1, 2) - image = torch.from_numpy(image) - return image - -control_image = make_inpaint_condition(init_image, mask_image) -``` - ---
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